Systems, methods, and apparatuses for monitoring weld quality

ABSTRACT

An arc welding system and methods. The system is capable of monitoring variables during a welding process, according to wave shape states, and weighting the variables accordingly, detecting defects of a weld, diagnosing possible causes of the defects, quantifying overall quality of a weld, obtaining and using data indicative of a good weld, improving production and quality control for an automated welding process, teaching proper welding techniques, identifying cost savings for a welding process, and deriving optimal welding settings to be used as pre-sets for different welding processes or applications.

RELATED APPLICATIONS

The present application is being filed as a continuation-in-part (CIP)patent application claiming priority to and the benefit of U.S. patentapplication Ser. No. 13/453,124, filed on Apr. 23, 2012, which is acontinuation-in-part of U.S. patent application Ser. No. 12/775,729, nowU.S. Pat. No. 8,569,646, filed on May 7, 2010, which claims priority toand the benefit of U.S. Provisional Patent Application No. 61/261,079filed on Nov. 13, 2009, the entire disclosures of which are incorporatedherein by reference.

TECHNICAL FIELD

The general inventive concepts relate to electric arc welding and, moreparticularly, to systems, methods, and apparatuses for monitoringvariables during a welding process and weighting the variablesaccordingly, quantifying weld quality, obtaining and using dataindicative of a good weld, improving production and quality control foran automated welding process, teaching proper welding techniques,identifying cost savings for a welding process, and deriving optimalwelding settings to be used as pre-sets for different welding processesor applications.

BACKGROUND

Many different conditions and parameters contribute to the overallquality of a resulting weld. Consequently, manufacturers of electric arcwelders have attempted to monitor operation of the welder to determinethe quality of the weld and the efficiency of the welder duringoperation in a manufacturing facility. One attempt to monitor anelectric arc welder is illustrated in U.S. Pat. No. 6,051,805 to Vaidya(hereinafter “Vaidya”) where a computer or other programmed instrumentis employed to monitor average current and the efficiency of the weldingoperation, which efficiency is expressed as a ratio of the time weldingis performed to the total time of the work shift. In accordance withstandard technology, this disclosed monitoring system includes a firstcontrol circuit which is in the form of a central processing unit withstandard accessories such as RAM and EPROM. A second control circuit isconnected to the first circuit to input and output information duringthe monitoring procedure. The monitor gathers information over a periodof time which is disclosed as extending over a few hours or up to 999hours. The monitor determines welding efficiency and monitors time todetermine average current and accumulated arc welding time for overallefficiency.

Vaidya discloses a capability of monitoring the current and wire feedspeed, as well as gas flow during the welding procedure. All of thisinformation is stored in appropriate memory devices for subsequentretrieval of the operating characteristics of the welder during thewelding process. In this way, the productivity of the welder can bemeasured to calculate cost efficiency and other parameters. Monitoringof the electric arc welder, as suggested in Vaidya, has been attemptedby other manufacturers to measure average current during a weldingprocess. However, measuring average current, voltage, wire feed speed orother parameters during a welding process and using this data forrecording the performance of the welding operation has not beensatisfactory. In the past, monitoring devices have had no pre-knowledgeof the parameters being monitored.

Consequently, monitoring of parameters such as current, voltage and evenwire feed speed in the past, even using the technology set forth inVaidya, has been chaotic in response and incapable of determining theactual stability of the electric arc or whether the welding process isabove or below desired parameter values. This information must be knownfor the purpose of rejecting a welding cycle and/or determining thequality of the weld performed during the welding cycle with desiredaccuracy. In summary, monitoring the operation of an electric arc welderwhen used for a variety of welding processes has not been satisfactorybecause there is no prior knowledge which can be used for the purposesof evaluating the welding process during its implementation.

Overcoming these drawbacks, U.S. Pat. No. 6,441,342 to Hsu (hereinafter“Hsu”) discloses a monitor and method of monitoring an electric arcwelder as the welder performs a selected arc welding process thatcreates information on the operation of the welder. Accordingly, use ofstandard, high power computer technology can be used on equally preciseand intelligent data generated by the monitor. The monitor andmonitoring system of Hsu employs known information during the weldingprocess. The information is fixed and not varying. The monitorconcentrates on specific aspects of the welding process to employ priorknowledge which is compared to actual performance. Thus, the stabilityand acceptable magnitudes or levels of a selected parameter isdetermined during a specific aspect of the welding process. The weldprocess is separated into fixed time segments with known desiredparameters before monitoring. Then this data can be processed by knowncomputer techniques to evaluate aspects of the weld cycles.

Hsu discloses that the welding process is carried out by an electric arcwelder generating a series of rapidly repeating wave shapes. Each waveshape constitutes a weld cycle with a cycle time. Each weld cycle (i.e.,wave shape) is created by a known wave shape generator used to controlthe operation of the welder. These wave shapes are divided into states,such as in a pulse welding process, a state of background current, rampup, peak current, ramp down, and then back to background current. Bydividing the known driving wave shape into states defined as timesegments of the generated arc characteristics, any selected one of thestates can be monitored. Indeed, many states can be multiplexed. Forinstance, in the pulse welding process the state related to the peakcurrent can be monitored. Hsu discloses that the state of the weldingprocess is monitored by being read at a high rate preferably exceeding1.0 kHz. Each of the actual welding parameters, such as current, voltageor even wire feed speed is detected many times during each peak currentstate of the wave shape used in the pulse welding process. In thismanner, the ramp up, ramp down, and background current are ignoredduring the monitoring process of the peak current state.

Consequently, the peak current is compared with a known peak current. Afunction of the peak current can be used to detect variations in theactual peak current output from the electric arc welder. In Hsu, aminimum level and a maximum level on the lower and higher side of thecommand peak current are used to determine the level of the peak currentmany times during each peak current state of the pulse weld wave shape.Whenever the current exceeds the maximum, or is less than the minimum,this event is counted during each wave shape. The total deviations orevents are counted for a weld time (i.e., a time during which a weldingprocess or some significant portion thereof is carried out). If thiscount is beyond a set number per wave shape or during the weld time, awarning may be given that this particular welding process experiencedunwanted weld conditions. Indeed, if the count exceeds a maximum levelthe weld is rejected. This same capability is used with a statisticalstandard deviation program to read the peak current many times duringeach peak current state of the wave shape to sense the magnitude of thestandard deviation. In practice, the standard deviation is theroot-mean-square (RMS) deviation calculation by the computer program. InHsu, the average peak current is calculated and recorded as well as thelevel conditions and the stability characteristics. The RMS of thecurrent or voltage is also determined for each of the states beingmonitored, for example, the peak current state of a pulse wave shape.While the peak current level or standard elevation is monitored, thebackground current stage can be monitored by current level and duration.

Hsu discloses selecting a state in the wave shape and comparing thedesired and known command signals for that state to the actualparameters of the welding process during that monitored state. Theselection is based on prior knowledge of the waveform generator. Forexample, at a specific wire feed speed WFS1, the waveform generator isprogrammed to adjust peak current to control arc length. The “informed”monitor then selects the peak current segment as the monitored state,when welding at this wire feed speed WFS1. At another wire feed speedWFS2, however, the waveform generator is programmed to adjust backgroundtime to control arc length (and not peak current). The “informed”monitor then selects the background time as the monitored state andparameter, when welding at this wire feed speed WFS2. In contrast, aposteriori monitor has no idea that at different wire feed speeds,different aspects of the waveform should be monitored to detect arcstability. Monitoring background time at wire feed speed WFS1 ormonitoring peak current at wire feed speed WFS2, in this example, wouldbe very ineffective. Thus, Hsu discloses using a time segment of thewave shape for monitoring this segment of the wave shape using priorknowledge of the desired values. This allows actual monitoring of theelectric arc welding process and not merely an averaging over the totalwave shape.

In Hsu, the monitor is characterized by the use of prior knowledge, asopposed to the normal process of merely reading the output parametersexperienced during the welding process. Consequently, the monitoringgreatly simplifies the task of detecting normal behavior of a welderwhen the normal behavior is a function of time and differs during onlyone aspect of the welding process. The teachings of Hsu are not asapplicable to monitoring voltage in a constant voltage process, becausethe desired level of voltage is a known characteristic during the totalweld cycle. However, in other welding processes when both the voltageand current vary during different segments of the wave shape, the methodof Hsu gives accurate readings of stability, RMS, standard deviation,average, below minimum and above maximum before the actual parameterbeing monitored during selected segments of the wave shape.

According to Hsu, the time varying welding processes, such as pulsewelding and short circuit welding, are monitored with precise accuracyand not by reading general output information. The monitor is activatedat a selected time in each wave form which is the selected state orsegment of the wave shape. The monitor compares actual parameters to thedesired parameters in the form of command signals directed to a powersupply of the welder. In Hsu, monitoring can occur during only specificsegments of the wave shape; however, in exceptional events, such as whenthe arc is extinguished or when there is a short circuit, a computerizedsubroutine is implemented by either voltage sensing or current sensingto restart the arc and/or correct the short. The subroutines for theseevents run parallel to the monitoring program. Consequently, theseexceptions do not affect the overall operation of the monitor. Thesesubroutines are constructed as exceptional states or time segments. Theparameters or signals within these exceptional states are monitored in asimilar fashion as described above.

In Hsu, production information over a calendar time, shift or even byoperator can be accumulated for the purposes of evaluating the operationor efficiency of a welder. The monitoring of each weld cycle bymonitoring a specific segment or state of the wave shape allowsaccumulation of undesired events experienced over time. This also allowsa trend analysis so that the operator can take corrective actions beforethe welding process actually produces defective production welds. Trendanalysis, defect analysis, accumulated defects, logging of all of theseitems and related real time monitoring of the electric arc welder allowsdirect intervention in a timely manner to take preventive actions asopposed to corrective actions.

SUMMARY

The general inventive concepts contemplate systems, methods, andapparatuses for monitoring variables during a welding process andweighting the variables accordingly, quantifying weld quality, obtainingand using data indicative of a good weld, detecting weld defects, anddiagnosing possible causes of the weld defects. The weld quality dataallows for improvements in production and quality control for anautomated welding process, teaching proper welding techniques,identifying cost savings for a welding process, and deriving optimalwelding settings to be used as pre-sets for different welding processesor applications. By way of example to illustrate various aspects of thegeneral inventive concepts, several exemplary systems, methods, and aredisclosed herein.

A method of monitoring an electric arc welder as the welder performs aselected arc welding process by creating actual welding parametersbetween an advancing wire and a workpiece, the selected processcontrolled by command signals to a power supply of the welder, accordingto one exemplary embodiment, is disclosed. The method includes (a)generating a series of rapidly repeating wave shapes, each wave shapeconstituting a weld cycle with a cycle time; (b) dividing the waveshapes into states; (c) measuring a selected weld parameter occurring inone of the wave shape states at an interrogation rate over a period oftime to obtain a data set for the selected weld parameter; (d) for eachperiod of time, calculating a stability value for the selected weldparameter from the data set; (e) comparing each stability value to anexpected stability value to determine if a difference between thestability value and the expected stability value exceeds a predeterminedthreshold; and (f) if the difference exceeds the threshold, weightingthe stability value with a magnitude weight based on the difference, andweighting the stability value with a time contribution weight based on atime contribution of the wave shape state to its wave shape. In thismanner, the method can assign multiple weights (e.g., based on adegree/magnitude of deviation and a time contribution of its state) to ameasured parameter (i.e., an item in the data set) that constitutes anoutlier. In one exemplary embodiment, an outlier is defined as ameasured value for a weld parameter that falls outside the limit ofthree (3) standard deviations away from the mean value of the weldparameter. A monitor, integrated with an arc welder, for performing thisexemplary method is also contemplated.

A method of quantifying a weld's quality by monitoring an electric arcwelder as the welder performs a selected arc welding process by creatingactual welding parameters between an advancing wire and a workpiece, theselected process controlled by command signals to a power supply of thewelder, according to one exemplary embodiment, is disclosed. The methodincludes: (a) generating a series of rapidly repeating wave shapes, eachwave shape constituting a weld cycle with a cycle time; (b) dividing thewave shapes into states; (c) measuring a plurality of selected weldparameters occurring in one or more of the states at an interrogationrate over a period of time repeatedly during a weld time; and (d)calculating a plurality of quality parameters for each of the statesbased on the measurements of the selected weld parameters during theperiods of time, wherein the quality parameters represent an overallquality measurement of the weld. A monitor, integrated with an arcwelder, for performing this exemplary method is also contemplated.

In one exemplary embodiment, the method also includes: (e) comparing avalue of each of the quality parameters calculated for each period oftime to a corresponding expected quality parameter value to determine ifa difference between the calculated quality parameter value and theexpected quality parameter value exceeds a predetermined threshold; and(f) if the difference exceeds the threshold, weighting the calculatedquality parameter value with a magnitude weight based on the difference,and weighting the calculated quality parameter value with a timecontribution weight based on a time contribution of its state to thewave shape including the state. A monitor, integrated with an arcwelder, for performing this exemplary method is also contemplated.

In one exemplary embodiment, the interrogation rate is 120 kHz. In oneexemplary embodiment, the period of time is approximately 250 ms.

In one exemplary embodiment, the selected weld parameters include, foreach of the states, a count of the measurements taken for each of theselected weld parameters in the period of time, a mean voltage voltagein the period of time, a root mean square voltage RMSV in the period oftime, a voltage variance V_(var) in the period of time, a mean currentcurrent in the period of time, a root mean square current RMSI in theperiod of time, and a current variance I_(var) in the period of time,wherein voltage=a sum of voltages measured in the period of time/thecount of voltage measurements, wherein

${RMSV} = \sqrt{\frac{\sum\limits_{i = 1}^{N}\;\left( {voltage}_{i} \right)^{2}}{N}}$wherein V_(var)=RMSV−voltage, wherein current=a sum of currents measuredin the period of time/the count of current measurements, wherein

${RMSI} = \sqrt{\frac{\sum\limits_{i = 1}^{N}\;\left( {current}_{i} \right)^{2}}{N}}$and wherein I_(var)=RMSI−current.

In one exemplary embodiment, the quality parameters include a qualitycount average QCA for each state calculated as:

${QCA} = \frac{\sum\limits_{i = 1}^{N}\;{count}_{i}}{N}$wherein N is the total number of weld cycles in a period of time, andwherein count.sub.i refers to a count of the measurements for a specificone of the weld cycles in the period of time.

In one exemplary embodiment, the quality parameters include a qualitycount standard deviation QCSD for each state calculated as:

${QCSD} = \frac{\sum\limits_{i = 1}^{N}\;\left( {{count}_{i} - {QCA}} \right)^{2}}{N - 1}$In one exemplary embodiment, the quality parameters include a qualitycount standard deviation QCSD for each state calculated as:

${QCSD} = \frac{\sum\limits_{i = 1}^{N}\;\left( {{count}_{i} - {QCA}} \right)^{2}}{N}$

In one exemplary embodiment, the quality parameters include a qualityvoltage average QVA for each state calculated as:

${QVA} = \frac{\sum\limits_{i = 1}^{N}\;{voltage}_{i}}{N}$wherein N is the total number of weld cycles in the period of time, andwherein voltage, refers to a voltage measurement for a specific one ofthe weld cycles in the period of time.

In one exemplary embodiment, the quality parameters include a qualityvoltage standard deviation QVSD for each state calculated as:

${QVSD} = \frac{\sum\limits_{i = 1}^{N}\;\left( {{voltage}_{i} - {QVA}} \right)^{2}}{N - 1}$In one exemplary embodiment, the quality parameters include a qualityvoltage standard deviation QVSD for each state calculated as:

${QVSD} = \frac{\sum\limits_{i = 1}^{N}\;\left( {{voltage}_{i} - {QVA}} \right)^{2}}{N}$

In one exemplary embodiment, the quality parameters include a qualitycurrent average QIA for each state calculated as:

${QIA} = \frac{\sum\limits_{i = 1}^{N}\;{current}_{i}}{N}$wherein N is the total number of weld cycles in the period of time, andwherein current, refers to a current measurement for a specific one ofthe weld cycles in the period of time.

In one exemplary embodiment, the quality parameters include a qualitycurrent standard deviation QISD for each state calculated as:

${QISD} = \frac{\sum\limits_{i = 1}^{N}\;\left( {{current}_{i} - {QIA}} \right)^{2}}{N - 1}$In one exemplary embodiment, the quality parameters include a qualitycurrent standard deviation QISD for each state calculated as:

${QISD} = \frac{\sum\limits_{i = 1}^{N}\;\left( {{current}_{i} - {QIA}} \right)^{2}}{N}$

In one exemplary embodiment, the quality parameters include a qualityvoltage variance average QVVA for each state calculated as:

${QVVA} = \frac{\sum\limits_{i = 1}^{N}\;{V{var}}_{i}}{N}$wherein N is the total number of weld cycles in the period of time.

In one exemplary embodiment, the quality parameters include a qualityvoltage variance standard deviation QVVSD for each state calculated as:

${QVVSD} = \frac{\sum\limits_{i = 1}^{N}\;\left( {{V{var}}_{i} - {QVVA}} \right)^{2}}{N - 1}$In one exemplary embodiment, the quality parameters include a qualityvoltage variance standard deviation QVVSD for each state calculated as:

${QVVSD} = \frac{\sum\limits_{i = 1}^{N}\;\left( {{V{var}}_{i} - {QVVA}} \right)^{2}}{N}$

In one exemplary embodiment, the quality parameters include a qualitycurrent variance average QIVA for each state calculated as:

${QIVA} = \frac{\sum\limits_{i = 1}^{N}\;{V{var}}_{i}}{N}$wherein N is the total number of weld cycles in the period of time.

In one exemplary embodiment, the quality parameters include a qualitycurrent variance standard deviation QIVSD for each state calculated as:

${QIVSD} = \frac{\sum\limits_{i = 1}^{N}\;\left( {{Ivar}_{i} - {QIVA}} \right)^{2}}{N - 1}$In one exemplary embodiment, the quality parameters include a qualitycurrent variance standard deviation QIVSD for each state calculated as:

${QIVSD} = \frac{\sum\limits_{i = 1}^{N}\;\left( {{Ivar}_{i} - {QIVA}} \right)^{2}}{N}$

Similar quality parameters based on monitored wire feed speed (WFS) mayalso be calculated in a similar manner such as, for example, a qualitywire feed speed average (QWA), a quality wire feed speed standarddeviation (QWSD), a quality wire feed speed variance average (QWVA), anda quality wire feed speed variance standard deviation (QWVSD).

In one exemplary embodiment, the method further includes: (e) using thequality parameters in a metric to evaluate subsequent welds. A monitor,integrated with an arc welder, for performing this exemplary method isalso contemplated.

A method of evaluating a plurality of welds performed undersubstantially the same conditions and according to substantially thesame arc welding process by monitoring an electric arc welder as thewelder performs the welds according to the arc welding process bycreating actual welding parameters between an advancing wire and aworkpiece, the selected process controlled by command signals to a powersupply of the welder, according to one exemplary embodiment, isdisclosed. The method includes, during each weld: (a) generating aseries of rapidly repeating wave shapes, each wave shape constituting aweld cycle with a cycle time; (b) dividing the wave shapes into states;(c) measuring a selected weld parameter occurring in one of the statesat an interrogation rate over a period of time to obtain a data set forthe selected weld parameter; (d) for each period of time, calculating aquality value for the selected weld parameter from the data set; (e)comparing each quality value to an expected quality value to determineif a difference between the quality value and the expected quality valueexceeds a predetermined threshold; (f) if the difference exceeds thethreshold, weighting the quality value with a magnitude weight based onthe difference, and weighting the quality value with a time contributionweight based on a time contribution of the state to its wave shape; and(g) using all of the quality values, including any weighted qualityvalues, obtained during the weld time to determine a quality score forthe weld.

In one exemplary embodiment, the method further includes: (h) rejectingthe weld if its quality score is within a first predefined range ofquality scores; and (i) accepting the weld if its quality score iswithin a second predefined range of quality scores.

In one exemplary embodiment, the method further includes: (h)permanently associating each weld with its corresponding quality score.

In one exemplary embodiment, the interrogation rate is 120 kHz. In oneexemplary embodiment, the period of time is approximately 250 ms.

In one exemplary embodiment, the selected weld parameter is arc current.In one exemplary embodiment, the selected weld parameter is arc voltage.

A method of providing instruction to an individual (i.e., an operator)manually performing an arc welding process using an electric arc welderincluding an integrated monitor, the welder performing the arc weldingprocess by creating actual welding parameters between an advancing wireand a workpiece, the monitor capable of monitoring the actual weldingparameters, and the arc welding process controlled by command signals toa power supply of the welder, according to one exemplary embodiment, isdisclosed. The method includes: (a) generating a series of rapidlyrepeating wave shapes, each wave shape constituting a weld cycle with acycle time; (b) dividing the wave shapes into states; (c) measuring aselected weld parameter occurring in one of the states at aninterrogation rate over a period of time to obtain a data set for theselected weld parameter; (d) for each period of time, calculating aquality value for the selected weld parameter from the data set; (e)comparing each quality value to an expected quality value to determineif a difference between the quality value and the expected quality valueexceeds a predetermined threshold; (f) if the difference exceeds thethreshold, weighting the quality value with a magnitude weight based onthe difference, and weighting the quality value with a time contributionweight based on a time contribution of the state to its wave shape; (g)using the quality value, including any weights, to update a currentaggregate quality score for the weld; (h) determining if the currentaggregate quality score is within a predefined range of acceptablequality scores during the welding process; and (i) if the currentaggregate quality score is outside the predefined range of acceptablequality scores, providing information on corrective action to theoperator.

In one exemplary embodiment, the interrogation rate is 120 kHz. In oneexemplary embodiment, the period of time is approximately 250 ms.

In one exemplary embodiment, the information is provided visually. Inone exemplary embodiment, the information is provided audibly.

In one exemplary embodiment, the information includes a suggested changein a position of the wire relative to the workpiece. In one exemplaryembodiment, the information includes a suggested change in a rate ofmovement of the wire relative to the workpiece.

In one exemplary embodiment, the information is provided to the operatorat a predetermined reporting rate. In one exemplary embodiment, thereporting rate is less than 30 seconds. In one exemplary embodiment, thereporting rate is greater than or equal to 30 seconds.

In one exemplary embodiment, the information is provided if recentchanges in the current aggregate quality score indicate the currentaggregate quality score is likely to move outside the predefined rangeof acceptable quality scores.

In one exemplary embodiment, the method further includes: (j) if thecurrent aggregate quality score is within the predefined range ofacceptable quality scores, providing confirmation to the operator thatno corrective action is necessary.

A method of evaluating a plurality of operators performing an arcwelding process by monitoring an electric arc welder associated witheach of the operators, as each welder is used by its respective operatorto perform said arc welding process by creating actual weldingparameters between an advancing wire and a workpiece with said arcwelding process controlled by command signals to a power supply of saidwelder, is disclosed. The method includes, for each operator: (a)generating a numerical score indicating a quality measurement of a weldformed according to said arc welding process relative to a predeterminedbaseline weld; (b) measuring an amount of time said operator spendsperforming said arc welding process; and (c) associating said numericalscore and said welding time with said operator.

In one exemplary embodiment, the numerical score is generated by: (a1))generating a series of rapidly repeating wave shapes, each wave shapeconstituting a weld cycle with a cycle time; (a2) dividing said waveshapes into states; (a3) measuring a selected weld parameter occurringin one of said states at an interrogation rate over a period of time toobtain a data set for said selected weld parameter; (a4) for each periodof time, calculating a quality value for said selected weld parameterfrom said data set; (a5) comparing each quality value to an expectedquality value to determine if a difference between said quality valueand said expected quality value exceeds a predetermined threshold; (a6)if said difference exceeds said threshold, weighting said quality valuewith a magnitude weight based on said difference, and weighting saidquality value with a time contribution weight based on a timecontribution of said state to its wave shape; and (a7) using all of saidquality values, including any weighted quality values, obtained duringsaid arc welding process to determine said numerical score.

A method of performing a cost-effective analysis for a selected arcwelding process, wherein an electric arc welder performs the arc weldingprocess by creating actual welding parameters between an advancing wireand a workpiece, the selected process controlled by command signals to apower supply of the welder, according to one exemplary embodiment, isdisclosed. The method includes: (a) identifying a plurality of weldconditions capable of affecting overall weld quality; (b) varying one ofthe weld conditions across a plurality of welds and fixing all remainingweld conditions across the welds; (c) for each of the welds: (i)generating a series of rapidly repeating wave shapes, each wave shapeconstituting a weld cycle with a cycle time; (ii) dividing the waveshapes into states; (iii) measuring a selected weld parameter occurringin one of the states at an interrogation rate over a period of time toobtain a data set for the selected weld parameter; (iv) for each periodof time, calculating a stability value for the selected weld parameterfrom the data set; (v) comparing each stability value to an expectedstability value to determine if a difference between the stability valueand the expected stability value exceeds a predetermined threshold; (vi)if the difference exceeds the threshold, weighting the stability valuewith a magnitude weight based on the difference, and weighting thestability value with a time contribution weight based on a timecontribution of the wave shape state to its wave shape; (vii) using thestability values obtained during the weld time, including any weightedstability values, to calculate an overall quality score for the weld;(viii) determining a cost for the weld; and (ix) associating the qualityscore and the cost with the weld.

In one exemplary embodiment, the weld conditions include one or more ofwire characteristics, workpiece characteristics, a shielding gas flowrate, a shielding gas composition, and a workpiece pre-heat temperature.

In one exemplary embodiment, the cost includes monetary expendituresrelated to producing the weld. In one exemplary embodiment, the costincludes a total time required to complete the weld.

In one exemplary embodiment, the stability value is a standardstatistical deviation for the selected weld parameter.

In one exemplary embodiment, the interrogation rate is 120 kHz. In oneexemplary embodiment, the period of time is approximately 250 ms.

In one exemplary embodiment, the method further includes: (d) outputtingthe quality score and the cost (or respective averages thereof)associated with each of the welds.

A method of using pre-set welding parameters to obtain a weld having adesired quality, the weld produced by an electric arc welder performinga selected arc welding process by creating actual welding parametersbetween an advancing wire and a workpiece, the welding processcontrolled by command signals to a power supply of the welder, accordingto one exemplary embodiment, is disclosed. The method includes: (a)presenting a plurality of sets of selected weld parameters to a useralong with a quality score corresponding to each set, wherein thequality score quantifies an overall quality of a weld previouslyobtained using the set of selected weld parameters; (b) receiving inputfrom the user as to which of the sets of selected weld parameters to usefor performing the welding process; and (c) performing the weldingprocess using the set of selected weld parameters corresponding to theinput.

In one exemplary embodiment, a cost associated with performing thewelding process using each of the sets of selected weld parameters ispresented to the user.

In one exemplary embodiment, the method further includes: (d) receivinginput from the user identifying a minimum acceptable quality score; and(e) filtering out all sets of selected weld parameters that correspondto an associated quality score below the minimum acceptable qualityscore.

In one exemplary embodiment, the method further includes: (d) receivinginput from the user identifying a range of acceptable quality scores;and (e) filtering out all sets of selected weld parameters thatcorrespond to an associated quality score outside of the range ofacceptable weld quality scores.

In one exemplary embodiment, a method of diagnosing an arc weldingprocess by monitoring an electric are welder as the welder performs thearc welding process by creating actual welding parameters between anadvancing wire and a workpiece to create a weld is disclosed. Thewelding process is controlled by command signals to a power supply ofthe welder. The method includes generating a series of rapidly repeatingwave shapes, each wave shape constituting a weld cycle with a cycletime, and dividing the wave shapes into states. The method furtherincludes measuring a plurality of weld parameters occurring in one ormore of the states at an interrogation rate over a period of timerepeatedly during the welding process. The method also includescalculating a plurality of quality parameters for each of the one ormore states based on the measurements of the weld parameters during thewelding process. The method further includes analyzing at least one ofthe plurality of quality parameters and the plurality of weld parametersto diagnose the arc welding process by determining one or more possiblecauses of one or more localized or continuous defects of the weld.

The method may further include comparing a value of each of the qualityparameters calculated for each period of time to a correspondingexpected quality parameter value to determine if a difference betweenthe calculated quality parameter value and the expected qualityparameter value exceeds a predetermined threshold. If the differenceexceeds the threshold, the method also includes weighting the calculatedquality parameter value with a magnitude weight based on the difference,and weighting the calculated quality parameter with a time contributionweight based on a time contribution of its state to the wave shapeincluding the state.

In one exemplary embodiment, a system for diagnosing an arc weldingprocess by monitoring an electric are welder as the welder performs thearc welding process by creating actual welding parameters between anadvancing wire and a workpiece to create a weld is disclosed. Thewelding process is defined by a series of rapidly repeating wave shapescontrolled by command signals to a power supply of the welder. Thesystem includes a logic state controller for segmenting the wave shapesinto a series of time segmented states and a circuit for selecting aspecific wave shape state. The system further includes monitoringdevices for monitoring a plurality of weld parameters occurring in oneor more of the states at an interrogation rate over a period of timerepeated during the welding process to obtain a data set for theplurality of weld parameters. The system also includes a circuit forcalculating a plurality of quality parameters for each of the statesbased on the monitored plurality of weld parameters. The system furtherincludes a diagnostic logic circuit for analyzing at least one of theplurality of quality parameters and the plurality of weld parameters todiagnose the arc welding process by determining one or more possiblecauses of one more localized or continuous defects of the weld.

The system may further include a circuit for comparing a value of eachof the quality parameters calculated for each period of time to acorresponding expected quality parameter value to determine if adifference between the calculated quality parameter value and theexpected quality parameter value exceeds a predetermined threshold. Thesystem may also include a circuit for weighting the calculated qualityparameter value with a magnitude weight based on the difference, andweighting the calculated quality parameter value with a timecontribution weight based on a time contribution of its state to thewave shape including the state, if the difference exceeds the threshold.

In one exemplary embodiment, a method of determining a quality of a weldby monitoring a welder as the welder performs a welding process bycreating actual welding parameters between an advancing wire and aworkpiece is provided. The welding process is defined by a series ofrapidly repeating wave shapes controlled by command signals to a powersupply of the welder. The method includes segmenting a wave shape,having a weld cycle with a cycle time, into a series of time-segmentedstates. The method also includes selecting a non-adaptive state from theseries of time-segmented states. The non-adaptive state represents asegment of the wave shape where the command signals remain invariableunder different weld conditions. The method further include measuring aplurality of weld parameters generated between the advancing wire andthe workpiece during the non-adaptive state at an interrogation rateover an interval of time. In addition, the method includes calculating aplurality of quality parameters for the non-adaptive state based onmeasurements of the plurality of weld parameters acquired during theinterval of time within the non-adaptive state.

In one exemplary embodiment, a system for determining a quality of aweld by monitoring a welder as the welder performs a welding process bycreating actual welding parameters between an advancing wire and aworkpiece. The welding process being defined by a series of rapidlyrepeating wave shapes controlled by command signals to a power supply ofthe welder. The system includes a logic state controller for segmentinga wave shape, having a weld cycle with a cycle time, into a series oftime-segmented states. The system further includes a selection circuitfor selecting a non-adaptive state from the series of time-segmentedstates. The non-adaptive state represents a segment of the wave shapewhere the command signals remain invariable under different weldconditions. The system also includes a monitor circuit configured tomeasure a plurality of weld parameters generated between the advancingwire and the workpiece during the non-adaptive state at an interrogationrate over an interval of time. In addition, the system includes acircuit for calculating a plurality of quality parameters for thenon-adaptive state based on measurements of the plurality of weldparameters acquired during the interval of time within the non-adaptivestate.

Numerous aspects of the general inventive concepts will become readilyapparent from the following detailed description of exemplaryembodiments, from the claims and from the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The general inventive concepts as well as embodiments and advantagesthereof are described below in greater detail, by way of example, withreference to the drawings in which:

FIG. 1 is a combined block diagram and computer flow chart or programillustrating a monitor of an arc welder, according to one exemplaryembodiment;

FIG. 2 is a current command graph from a wave generator showing thecommand wave shape divided into time segments or states of both fixedand variable durations, according to one exemplary embodiment;

FIG. 3 is a current graph of the actual command signals for arc currentwith the actual arc current parameter superimposed in dashed lines,according to one exemplary embodiment;

FIG. 4 is a block diagram of an aspect of the invention for monitoringsignals internal of the welder instead of weld parameters as illustratedin FIGS. 2 and 3, according to one exemplary embodiment;

FIG. 5 is a time based graph illustrating the wave shape, wire feedercommand signal and actual wire feeder command signal as experienced inthe exemplary embodiment shown in FIG. 4;

FIG. 6 is a portion of a parameter curve illustrating a level monitoringfeature, according to one exemplary embodiment;

FIG. 7 is a block diagram and computer flow chart or programillustrating processing for stability during a selected state of thewave shape shown in FIGS. 2 and 3, according to one exemplaryembodiment;

FIG. 8 is a block diagram and computer flow chart or program to processinformation from the level monitor stages of the exemplary embodimentshown in FIG. 1;

FIG. 9 is a flowchart illustrating a weighting method for weightingsampled weld data parameters, according to one exemplary embodiment;

FIG. 10 is a diagram of a conceptual production line, according to oneexemplary embodiment;

FIG. 11 is a flow chart illustrating a method of instruction, accordingto one exemplary embodiment;

FIG. 12 is a block diagram illustrating a system for monitoringstudents, according to one exemplary embodiment.

FIG. 13 is a flow chart illustrating a method of monitoring students,according to one exemplary embodiment.

FIGS. 14A and 148 are tables showing exemplary data used in a costanalysis for a welding process, according to one exemplary embodiment;

FIG. 15 is a table showing pre-set data associating welding conditions,welders, and welding processes, according to one exemplary embodiment;

FIG. 16 illustrates a schematic block diagram of an embodiment of asystem for diagnosing an arc welding process;

FIG. 17 is a flowchart of a method of diagnosing an arc welding processusing the system of FIG. 16 by monitoring an electric arc welder as thewelder performs the arc welding process by creating actual weldingparameters between an advancing wire and a workpiece to create a weld;

FIG. 18 illustrates a schematic block diagram of an embodiment of asystem for adjusting welding process monitoring and evaluation inresponse to different or varying welding conditions;

FIG. 19 illustrates a schematic block diagram of an exemplary,non-limiting embodiment of a monitor from the system of FIG. 18according to one or more aspects; and

FIG. 20 illustrates a flowchart of a method for determining a quality ofa weld by monitoring a welder as the welder performs a welding processby creating actual welding parameters between an advancing wire and aworkpiece.

DETAILED DESCRIPTION

While the general inventive concepts are susceptible of embodiment inmany different forms, there are shown in the drawings and will bedescribed herein in detail specific embodiments thereof with theunderstanding that the present disclosure is to be considered as merelyan exemplification of the principles of the general inventive concepts.Accordingly, the general inventive concepts are not intended to belimited to the specific embodiments illustrated herein. Furthermore, thedisclosures of U.S. Pat. Nos. 5,278,390 and 6,441,342 are incorporatedherein by reference, in their entirety, as they may provide backgroundthat facilitates a better understanding of particular aspects and/oradvancements of the general inventive concepts.

The following are definitions of exemplary terms used throughout thedisclosure. Both singular and plural forms of all terms fall within eachmeaning:

“Logic,” synonymous with “circuit” as used herein includes, but is notlimited to, hardware, firmware, software and/or combinations of each toperform a function(s) or an action(s). For example, based on a desiredapplication or needs, logic may include a software controlledmicroprocessor, discrete logic such as an application specificintegrated circuit (ASIC), or other programmed logic device. In someinstances, logic could also be fully embodied as software.

“Software” or “computer program” as used herein includes, but is notlimited to, one or more computer readable and/or executable instructionsthat cause a computer or other electronic device to perform functions,actions, and/or behave in a desired manner. The instructions may beembodied in various forms such as routines, algorithms, modules orprograms including separate applications or code from dynamically linkedlibraries. Software may also be implemented in various forms such as astand-alone program, a function call, a servlet, an applet, instructionsstored in a memory, part of an operating system or other type ofexecutable instructions. It will be appreciated by one of ordinary skillin the art that the form of software is dependent on, for example,requirements of a desired application, the environment it runs on,and/or the desires of a designer/programmer or the like.

“Computer” or “processing unit” as used herein includes, but is notlimited to, any programmed or programmable electronic device that canstore, retrieve, and process data.

Referring now to the drawings which illustrate various exemplaryembodiments of the general inventive concepts and applications employingthe general inventive concepts, FIG. 1 shows a block diagram and flowchart or program implemented by a standard onboard computer in electricarc welder 10. For example, welder 10 can be a Power Wave, inverterbased electric arc welder sold by The Lincoln Electric Company ofCleveland, Ohio. In accordance with standard technology, welder 10includes a three phase electrical input L1, L2, L3 directing electricalcurrent to power supply 12. An onboard computerized controller operatesthe inverter based power supply to create a positive potential atterminal 14 and a negative potential at terminal 16.

Selected arc welding processes are performed by directing a selectedpreviously determined wave shape to the actual welding circuit, shown tohave a standard smoothing inductor 18. Welder 10 performs the electricarc welding process between an advancing welding wire 20 from reel 22driven at a desired rate by feeder 24 operated at the speed of motor 26.Heat of the arc melts wire 20 and workpiece 30 to deposit molten metalfrom the wire onto the workpiece. To monitor the actual parameters ofthe welding process, shunt 32 (a monitoring device) provides outputsignal I_(a) from block 34 on line 34 a. This signal is representativeof the actual arc current at any given time. In a like manner, thevoltage between wire 20 and workpiece 30 is sensed by block 36 (amonitoring device) so the output V_(a) on line 36 a is the instantaneousarc voltage to constitute a second weld parameter. The weld parametersillustrated in FIG. 1 are the actual arc current I_(a) and the actualarc voltage V_(a).

Another parameter controlled for practicing the invention is wire feedspeed (WFS), caused by rotation of the motor 26. Consequently, threeexternally readable welding parameters of the welding process are arccurrent I_(a) in line 34 a, arc voltage V_(a) in line 36 a and the wirefeed speed WFS readable in line 46 b, as explained later. The WFS inline 46 b is read by tachometer or encoder 46 c (a monitoring device)connected to the drive rolls 24 of the feeder gear box or,alternatively, on a passive wheel attached to the wire. In FIG. 1, thetachometer is shown as driven by the feed rolls. It could also bedriven, for example, by the output shaft of motor 26.

The Power Wave electric arc welder includes a wave shape generator tocreate a series of rapidly repeating wave shapes, each wave shape (e.g.,a single sequence of a voltage/current waveform) constituting a weldcycle with a cycle time. These weld cycles are repeated during thewelding process to define a weld time. One embodiment of the Power Wavewelder 10 is shown in U.S. Pat. No. 5,278,390 to Blankenship wherein thewelder controls the individual wave shape to be output by power supply12 through command line 42 and the speed of motor 26 through commandline 44. Command line 44 has a signal which is recognized by themicroprocessor on the wire drive control 46 of motor 26 to output themotor voltage drive PWM pulses in line 46 a. In practice, theinformation on line 44 is digital and the command signal on line 46 a isanalog. Wave shape generator 40 creates digital signals in lines 42, 44to controlling the desired welding process to be performed by welder 10.The external parameters I_(a), V_(a) and WFS can be read by appropriatemonitoring devices.

The wave shape generator 40 divides or segments each of the output waveshapes into a series of time segmented portions or states. In oneexemplary embodiment, monitor M is a program loaded into the computer ofwelder 10, among other things, to read parameters during one selectedsegment of the wave shape. The monitor M can be implemented usingsoftware, hardware, and combinations thereof, without departing from thespirit and the scope of the general inventive concepts. The portion ofthe wave shape being monitored is determined by the wave shape generator40. Indeed, monitor M monitors various time segments or states of thewave shape output by generator 40. In practice, the wave shape generator40 selects several of the time segments forming the wave shape andoutputs the various states into a command interface 70. Consequently,the command interface 70 causes measurement of the parameters duringselected time segments of each wave shape output by the generator.Information or data on the command interface 70 includes the state orstates being monitored and the particular value or level of the variousparameters I_(a), V_(a), and/or WFS.

Interface 70 of monitor M contains the data recognizing the particularstate being processed together with the values for the weld parametersbeing read. The data in interface 70 is analyzed by level stage 81 todetermine the relationship of a parameter on a level basis. The actualparameters are compared with trained or measured parameters duringselected states of the wave shape from generator 40. During a particularsegment or state of the wave shape, level monitor stage 81 reads theactual parameters in lines 34 a, 36 a and 46 b. These instantaneousvalues of the actual parameters are stored in internal memory,identified as the report logic 82. The reading of the actual parametersoccurs rapidly as indicated by oscillator 84. In one exemplaryembodiment, reading of the actual parameters occurs at a rate of 120 kHzfor pulse welding. The rate can be adjusted; however, the higher therate the better the sensitivity of the level measurement. Level monitor81 also determines a deviation of the actual welding parameters fromeither a minimum or maximum level. In this manner, not only can theactual values be stored, but data is stored representing deviation ofthe actual reading of the parameter for a given state as compared to aminimum level or to a maximum level. Report memory or logic 82 recordsdeviation from a set level during a given state of the wave shape, aswell as the actual level during the selected state of the wave shape.For a total weld cycle, these readings are accumulated, counted orotherwise processed to determine the quality of the weld and any trendstoward weld defects.

In one exemplary embodiment, the readings (e.g., periodicallyaccumulated sets of the readings) are weighted based on a plurality ofcriteria. The readings can be accumulated, for example, every 250 ms. Inone exemplary embodiment, a set is weighted based on a magnitude of itsdeviation from an expected value (e.g., predetermined threshold, meanvalue) and a time contribution of its time segment to the correspondingwave shape. Such a weighting method (e.g., the weighting method 900shown in FIG. 9 and described below) could be implemented, for example,in level monitor stage 81 or any similar or related data processingstage.

Stability monitor stage 91 reads the actual welding parameters on lines34 a, 36 a and 46 b at a rapid rate determined by oscillator 94. In oneexemplary embodiment, reading of the actual parameters occurs at a rateof 120 kHz for pulse welding. Stability monitor stage 91 analyzes theactual weld parameters for standard deviation or absolute deviationduring a state of the wave shapes being output. Report memory or logic92 records this deviation during a given state of the wave shape, aswell as the actual value during the selected state of the wave shape.For a total weld cycle, these readings are accumulated, counted orotherwise processed to determine the quality of the welding and anytrends toward weld defects.

In one exemplary embodiment, the readings (e.g., periodicallyaccumulated sets of the readings) are weighted based on a plurality ofcriteria. The readings can be accumulated, for example, every 250 ms. Inone exemplary embodiment, a set is weighted based on a magnitude of itsdeviation from an expected value (e.g., predetermined threshold, meanvalue) and a time contribution of its time segment to the correspondingwave shape. Such a weighting method (e.g., the weighting method 900shown in FIG. 9 and described below) could be implemented, for example,in stability monitor stage 91 or any similar or related data processingstage.

A few wave shapes can be skipped when using either monitor stage 81 ormonitor stage 91. In one exemplary embodiment, after a start sequence,all of the wave shapes are monitored for analyzing the actual weldingparameters during the various selected states of the wave shape. Severalstates of a given wave shape in a welding process are monitored and theresults are recorded separately for each state to be analyzed for levelconformity, trend and stability. When measuring stability, a standarddeviation algorithm is used in monitor M to evaluate I_(a), V_(a) and/orWFS. This information is available to analyze each of the varioussegments of the wave shape forming a total weld cycle with a given cycletime. In practice, certain states, such as the peak current during apulse wave shape are monitored to determine the stability and leveldeviations of the pulse welding process. In an STT welding process,monitor M records short circuit times for each wave shape, since thesesegments vary in time according to the external conditions of thewelding process. Variation in short circuit time informs the weldingengineer of adjustments to be implemented.

The series of rapidly repeating wave shapes generated by the standardwave shape generator 40 are divided into time states, as shown in FIGS.2 and 3. The output current command wave shape is pulse wave shape 100with a peak current 102 having a fixed duration of time segment A shownin FIG. 3 and a background current 104 with a variable time duration forsegment B shown in FIG. 3. The wave shape is divided into segments attimes t₁-t₄ so that the command interface 70 receives the particularstate being processed by generator 40 at any given time. As shown inFIG. 3 by the dashed line 110, the actual arc current from shunt 33 inFIG. 1 deviates from the command current signal of wave shape 100.

During the selected functional states, such as state A or state B, theactual arc current I_(a) is read at a rate determined by oscillator 84or oscillator 94. In practice, this is a single software oscillator.Level monitor stage 81 records deviation in the ordinate directionbetween the actual parameter 110 and the command level of wave shape100. During the selected state, stability monitor stage 91 reads thestatistical standard deviation of the actual parameter. States A and Bare normally monitored for a pulse welding process. However, the ramp upstate between t₁-t₂ and/or the ramp down state during t₃-t₄ can bemonitored to control or at least read the activity of the actualparameter during these states of the wave shape. As illustrated, thebackground time segment B has a variable time, as shown by the variabletime positions of time t₁. Consequently, the state being monitored canhave a fixed time duration or a variable duration. When a variableduration, the state is monitored until the end of the duration. Reportlogic 82 senses this as a level from one time, i.e. t₄, to thesuccessive time, i.e., t₁. As the time t₁ changes with respect to thetime t₄, this time of each wave shape is recorded as a level which iscompared to a known time, obtained from interface 70 by selection of thewelding mode of generator 40.

Monitor M monitors the actual welding parameters during specificselected states of the wave shapes; however, the monitor also hasprogramming to operate the computer to determine the stability and/orlevel characteristics of an internal signal, such as the actual input tomotor 26 on line 46 a. Such internal monitoring of the signal on line 46a is set forth in the flow chart shown in FIG. 4 utilizing the signalsshown in FIG. 5.

The microprocessor in the wire feeder includes a subroutine that is aPID comparing network similar to an error amplifier. This PID comparatoris schematically illustrated as block 152 in FIG. 4 having a first input46 b which is a wire feed speed WFS and a command signal on line 44. Theactual WFS on line 46 b is read by a tachometer or encoder connected tothe drive rolls 24 of the feeder gear box or, alternatively, on apassive wheel attached to the wire to read the WFS. The output 156 ofthe PID is the voltage level at the input of the pulse width modulator158 which is digitized in the microprocessor of the feeder. The outputof the pulse width modulator is the command signal on line 46 a to motor26 for controlling the wire feed speed of feeder 24.

In accordance with one exemplary embodiment, monitor M includes theprocess program as schematically illustrated in FIG. 4 wherein thesignal on line 156 is read by processing block 160 and the results areoutput on line 162 to the input of the level monitor stage 81 and/or thestability monitor stage 91, as previously discussed with respect to theembodiment shown in FIG. 1. Consequently, an internal signal on line 156is read at a rapid rate, exceeding 1 kHz, to check the level of thisinternal signal and/or the stability of this signal.

As illustrated in FIG. 5, the wave shape 100 for pulse welding extendsas a succession of wave shapes from generator 40. With respect to thewire feed speed, the command signal from generator 40 on line 44 takesthe form shown in FIG. 5. It includes a start ramp up portion 170 and anending ramp down portion 172. These two portions cause a drasticincrease or decrease in the command signal on line 44. Between theseabnormal command portions of the signal on line 44, there is a generallylevel wire feed speed command which is employed for the purposes oftesting stability and/or the level deviation of this internal signal online 156. In FIG. 5, the wire acceleration portion 170 is held until thespeed is stabilized. This time is also monitored. Other internal signalscan be monitored using the same concept as shown in FIGS. 4 and 5. Thelevel monitor stage determines if the signal on line 156 exceeds theminimum or maximum for a prolonged time. For the wire feeder, thisnormally indicates a jam in the feeder system.

FIG. 6 shows the concept of a level monitor stage wherein threshold 180is the maximum parameter level and threshold 182 is the minimumparameter level. When the parameter, illustrated as arc current, exceedsthreshold 180 as indicated by transient 184, there is a recorded eventof over current. In a like manner, when the current is less than theminimum level 182, as shown by transient 186, there is recorded an undercurrent event. Additionally, these events can be weighted based on aplurality of criteria. In one exemplary embodiment, each event isweighted based on a magnitude of its deviation from an expected value(e.g., predetermined threshold, mean value) and a time contribution ofits time segment to the corresponding wave shape. Such a weightingmethod (e.g., the weighting method 900 shown in FIG. 9 and describedbelow) could be implemented, for example, in level monitor stage 81,stability monitor stage 91, or any similar or related data processingstage.

The weighted events are counted or otherwise accumulated periodically toprovide the output of the level monitor stage 81 as shown in FIG. 1. Theweighted events can be accumulated, for example, every 250 ms.Consequently, the level monitor stage 81 detects excursions 184 above apreset threshold and excursions 186 below a preset level. These levelsare set by the particular state in the interface 70. Some states of awave shape employ the level monitor stage 81 with thresholds and otherstates of the same wave shape may use the stability monitor stage 91.Preferably, and in practice, both monitor stages are used for theselected state or states of the wave shape being interrogated by monitorM.

The embodiment shown in FIG. 1 monitors the level and/or stability ofactual parameters for internal control signals during a selected stateof the wave shape from generator 40 or during the total weld asexplained in relationship to the disclosure in FIGS. 4 and 5. Themonitor M in FIG. 1, as so far explained, provides weighted data for usein analyzing the weld cycle or the total operation of the welder over awork period of time. Various analysis programs are used to process dataafter the data has been determined and stored. In accordance with oneexemplary embodiment, the weighted stability data from monitor stage 91is analyzed by two programs as shown in FIG. 7. It is within the skillof the art to analyze the stability data in a variety of computerprograms for recording, display and process intervention or evaluation.

As shown in FIG. 7, analysis program 200 uses the results of monitorstage 91 of monitor M (i.e., the weighted stability values). As anexample, the program 200 is operated during monitoring of the time statebetween times t₂-t₃, which is the current peak portion of the wave shapeas shown in FIGS. 2 and 3. Analysis program 200 is shown as a computerflow chart showing two systems employed to analyze the results of thestability stage 91 during the peak current state where the statisticalstandard deviation of actual current in line 34 a is calculated. Inpractice, there is a slight delay before the monitor stage 91 makescalculated deviations. The sample select feature to read I_(a) duringstate t₂-t₃ but ignore I_(a) otherwise is illustrated as sample selectoror filter 90 a. This program delay at the start of time segment t₂-t₃incorporated in filter 90 a allows the monitor to ignore fluctuations inthe current which are experienced during each level shift in the variousstages of the output wave shape.

In the programmed flow chart shown in FIG. 7, the stability output frommonitor stage 91 is read by the computer program shown as block 210which is reset as indicated by the logic on line 210 a at the end ofeach wave shape determined by the existence of time t₃. Consequently,the stability of each wave shape is captured by block 210. This capturedstability data is processed in accordance with two separate analysisprograms.

The first program includes the pass analysis routine 212. If thestability for a given wave shape passes the desired threshold set inblock 212, this information is output on line 214. If the particularwave shape has a stability less than a desired threshold, a logic signalappears in line 216. Counters 220, 222 are enabled by the logic on line224 during each of the weld cycles. Consequently, the stability passsignals for each of the wave shapes during the weld cycle are counted ineither counter 220 or counter 222. Of course, the first portion of eachstate t₂-t₃ is ignored to allow the parameter I_(a) to settle. Theresults of the two counters are read, stored or otherwise retained asindicated by the read block 220 a, 222 a, respectively. In one exemplaryembodiment, if the instability accumulated by counter stage 222 isbeyond a desired number, the weld cycle is rejected as indicated byblock 226.

A second analysis implementation of computer program 200 shown in FIG. 7is illustrated as block 230. This is a program enabled during the weldcycle. The total instability of the weld cycle accumulating during allwave shapes is analyzed as a total number wherein 100 is the most stablearc. The output of this stability accumulator and analyzing stage isread, stored or otherwise retained as indicated by block 236. If thereading stage 234 is below a set stability then the weld cycle isrejected as indicated by block 238. A person skilled in the art candesign other programs for analyzing the results of the monitor M fromstability stage 91. The computer program 200 exhibits twoimplementations to analyze the obtained weighted stability data. The twoimplementations can be selectively enabled (either one or the other orboth) depending on the nature of the arc stability or weld qualityproblem the monitor is configured to detect. It is advantageous to readstability in only selected states of the wave shapes, because stabilityover a variable pulse is not obtainable.

In accordance with another exemplary embodiment, the computer programfor analyzing the results of level monitor stage 81 of monitor M (i.e.,the weighted read values) is shown in FIG. 8. In this illustratedembodiment, level analysis program 250 processes the output from monitorlevel stage 81 in two separate routines, identified as a minimum monitorstage 81 a with filter 80 c and a maximum monitor stage 81 b with filter80 d. Either one of these stages can be used separately or, in practice,they are combined. Subsection 81 a relates to the determination oftransitions 186 shown in FIG. 6 which is an event where the actualparameter is below the threshold minimum 182. The minimum level on line202 a from generator 40 is used when stage 81 a is selected by programstep 252. These events are counted by block 254 for each of the weldcycles as indicated. The counter is enabled during the weld cycle by thelogic on line 254 a. Counter 254 is a running total of the wave shapesused in a weld cycle. The number of wave shapes is obtained by countingthe occurrences of time t₃ from the output of generator 40 as indicatedby line 258. As indicated before, the first part of the state isgenerally ignored to remove normal inconsistencies at the start of anyparticular state. Block 260 is the computer flow chart subroutine fordividing the accumulated minimum events 186 from monitor stage 81 adivided by the number N from the counter 256. This provides an averageof minimum transitions during the weld cycle, which is provided tosubroutine 262. The average minimum transitions are read, stored orotherwise output as indicated by block 262 a. If the average is above acertain threshold number provided by the wave generator or by theprogram step 264, program routine 266 determines that the weld cycle isunacceptable. If acceptable, no action is taken. However, if theacceptable routine 266 determines that the average is merely approachingthe number 264, a warning signal is provided by block 266 a. Totalunacceptability provides a weld reject signal by routine 266 b. A personskilled in the art can devise other computer programs for effecting theanalysis of the minimum current deviation or transition of the actualparameter as it relates to a set threshold.

In FIG. 8, the maximum monitor stage 81 b operates in conjunction withthe minimum stage 81 a. The maximum level is on line 202 b fromgenerator 40 and is used when stage 81 b is selected by program 270.Like data information and programming retains the same numbers. Counter272 counts the number of events 184 during the state t₂-t₃. Subroutine280 provides the average of events 184 during the various wave shapesformed during the weld cycle. This average in block 282 is read, storedor otherwise used as indicated by block 282 a. In block 286, theacceptability subroutine is processed wherein the number indicated byblock 284 output from generator 40 or otherwise implemented by computerprogram is compared with the average from block 282 to provide a warningsignal as indicated by block 286 a when the average approaches the setnumber indicated by block 284. If the number is reached, a rejectsubroutine is implemented as indicated by block 286 b.

In practice, stage 81 a and stage 81 b are implemented together and theaverage of both transitions from blocks 262 and 282 are analyzed by aread, acceptable number to give a warning and/or a rejection of a givenweld cycle. Consequently, in practice, minimum level deviations areanalyzed, maximum level deviations are analyzed, and total leveldeviations are analyzed. All of this is accomplished by the computerprogram as schematically illustrated in FIG. 8. The level stages 81 a,81 b output level conditions which are stored and/or displayed asdiscussed with report logic 82. The level conditions output by the levelstages 81 a, 81 b can be weighted, as discussed herein.

In view of the above, the use of the magnitude and time contributionweights provide a more accurate measure of parameter stability and,thus, overall weld quality. In this manner, an easy to understandnumerical value or score can be computed to quantify the overall qualityof a weld. In one exemplary embodiment, a weld score between 0-100 or0%-100% is calculated for a weld based on monitored welding conditionsor parameters, such as those monitored by the exemplary embodiment shownin FIG. 1. Such a weighting method (e.g., the weighting method 900 shownin FIG. 9 and described below) could be implemented, for example, inlevel monitor stage 81, stability monitor stage 91, or any similar orrelated data processing stage.

A weighting method 900, according to one exemplary embodiment, is shownin FIG. 9. The weighting method can be implemented, for example, in themonitor M. In an initial step 902 of the weighting method 900, wavesshapes of a weld cycle are divided into a series of time segmentedportions or states. Then, in step 904, weld parameters (e.g., voltage,amperage) corresponding to at least one of the states are sampled at agiven rate. In one exemplary embodiment, the sampling rate is 120 kHz.In one exemplary embodiment, the sampling rate is greater than or equalto 120 kHz. In one exemplary embodiment, the sampling rate can be usedto generate an interrupt for interrupt service routine (ISR) processing.

The sampled weld parameters are used to calculate weld data. In theexemplary weighting method 900, the weld data include an executioncount, a voltage sum, a voltage squared sum, an amperage sum, and anamperage squared sum. The execution count starts at zero and getsincremented by one for each sampling period (e.g., every 120 kHz). Thevoltage sum and the amperage sum start at zero and get increased by thesampled voltage and the sampled amperage, respectively, at each samplingperiod. Similarly, the voltage squared sum and the amperage squared sumstart at zero and get increased by the square of the sampled voltage andthe square of the sampled amperage, respectively, at each samplingperiod.

After a predefined sampling period, in step 906, the sampled weld datais passed on for further processing (as described below), the weld datavalues are reset to zero, and the sampling process (i.e., step 904) isrepeated. In one exemplary embodiment, the sampling period is 250 ms.Each collection of sampled weld data forms an analysis packet. Afterfurther processing of the analysis packet (e.g., every 250 ms),additional weld data is available representing a current weld qualityrating for the corresponding state. This additional weld data could begraphed and/or averaged. The average of these ratings over the length ofthe weld (i.e., the weld cycle) provides an overall quality indicatorfor the weld.

The further processing of the weld data of each analysis packet thatoccurs in step 906, for each of the sampled states, results in thecalculation of additional weld data. The additional weld data include anexecution count, a voltage average, a voltage root mean square (RMS), avoltage variance, an amperage average, an amperage RMS, and an amperagevariance. The value of the execution count of the additional weld datais copied from the value of the execution count of the weld data. Thevoltage average is calculated as the voltage sum (from the weld data)divided by the execution count. The voltage RMS is calculated as thesquare root of the quotient obtained by dividing the voltage squared sum(from the weld data) by the execution count. The voltage variance iscalculated as the voltage RMS minus the voltage average. The amperageaverage is calculated as the amperage sum (from the weld data) dividedby the execution count. The amperage RMS is calculated as the squareroot of the quotient obtained by dividing the amperage squared sum (fromthe weld data) by the execution count. The amperage variance iscalculated as the amperage RMS minus the amperage average.

After step 906, subsequent processing depends on whether the currentweld is a training weld to be used in determining weld qualityparameters or a normal weld to be evaluated against such weld qualityparameters. Thus, in step 908, it is determined whether the current weldis a training weld or a normal weld. In one exemplary embodiment, thedefault condition is that a weld is a normal weld unless otherwiseindicated (e.g., by user input).

If the current weld is determined in step 908 to be a training weld, thefollowing additional weld data values are saved for a significantportion of the training weld (e.g., 20-30 seconds): the execution count,the voltage average, the voltage variance, the amperage average, and theamperage variance, whereas the other weld data values and additionalweld data values can be disregarded. The significant portion of thetraining weld is the training period. In one exemplary embodiment, thetraining period corresponds to at least 80 consecutive analysis packets(i.e., sampling periods).

Thereafter, in step 910, weld quality parameters are calculated usingthe additional weld data values saved during the training period. Forexample, the following weld quality parameters are calculated for eachof the sampled states: a quality execution count average, a qualityexecution count standard deviation, a quality voltage average, a qualityvoltage standard deviation, a quality amperage average, a qualityamperage standard deviation, a quality voltage variance average, aquality voltage variance standard deviation, a quality amperage varianceaverage, and a quality amperage variance standard deviation.

The quality execution count average is calculated as the average of theexecution counts from all of the analysis packets processed during thetraining period. The execution counts could be rounded to integers. Thequality execution count standard deviation is calculated as the standarddeviation of the execution count from each analysis packet processedduring the training period relative to the quality execution countaverage. The quality voltage average is calculated as the average of thevoltage averages from all of the analysis packets processed during thetraining period. The quality voltage standard deviation is calculated asthe standard deviation of the voltage average from each analysis packetprocessed during the training period relative to the quality voltageaverage. The quality amperage average is calculated as the average ofthe amperage averages from all of the analysis packets processed duringthe training period. The quality amperage standard deviation iscalculated as the standard deviation of the amperage average from eachanalysis packet processed during the training period relative to thequality amperage average. The quality voltage variance average iscalculated as the average of the voltage variances from all of theanalysis packets processed during the training period. The qualityvoltage variance standard deviation is calculated as the standarddeviation of the voltage variance from each analysis packet processedduring the training period relative to the quality voltage variance. Thequality amperage variance average is calculated as the average of theamperage variances from all of the analysis packets processed during thetraining period. The quality amperage variance standard deviation iscalculated as the standard deviation of the amperage variance from eachanalysis packet processed during the training period relative to thequality amperage variance. As noted above, these quality parameters,when based on delivery of a confirmed good or otherwise acceptable weld,can be used as benchmarks to measure or otherwise rate subsequent welds.

If the current weld is determined in step 908 to be an evaluation weld(i.e., a weld requiring evaluation of its quality), as opposed to atraining weld, none of the weld data or additional weld data need besaved. Instead, the results of various quality calculations are obtainedand saved. These quality calculations include initially detecting, instep 914, the presence of various outliers. An outlier is a data pointor value that is more than a threshold distance from a mean value towhich the data point or value contributes. In one exemplary embodiment,an outlier is a value that falls outside the limit of three standarddeviations from the mean value.

In the weighting method 900, the outliers sought in step 914 includeexecution outliers, voltage outliers, voltage variance outliers,amperage outliers, and amperage variance outliers. For each of themonitored states, each of the analysis packets are evaluated to detectthe presence of any of these outliers.

If an analysis packet satisfies the following relationship, it isconsidered an execution outlier: absolute value of (executioncount-quality execution count average)>(3×quality execution countstandard deviation). If an analysis packet satisfies the followingrelationship, it is considered a voltage outlier: absolute value of(voltage average-quality voltage average)>(3×quality voltage standarddeviation). If an analysis packet satisfies the following relationship,it is considered a voltage variance outlier: absolute value of (voltagevariance-quality voltage variance average)>(3×quality voltage variancestandard deviation). If an analysis packet satisfies the followingrelationship, it is considered an amperage outlier: absolute value of(amperage average-quality amperage average)>(3×quality amperage standarddeviation). If an analysis packet satisfies the following relationship,it is considered an amperage variance outlier: absolute value of(amperage variance-quality amperage variance average)>(3×qualityamperage variance standard deviation).

After detection of these outliers, a two-step weighted sum (i.e., fromsteps 916 and 918) of each outlier is used in calculating a qualityindicator for the corresponding analysis packets.

The first step (i.e., step 916) in weighting each of the outliers isdetermined by the magnitude of the outlier relative to a three standarddeviation limit. In general, approximately 0.3% of the data points orvalues could fall outside the limit of three standard deviations and,thus, be considered an outlier. The weighting of the outlier increasesas its value increases above the limit of three standard deviations. Theoutlier is weighted at a full 100% at four standard deviations and isweighted at a maximum of 200% at five standard deviations. In general,the probability of a fully (i.e., 100%) weighted outlier occurring in anormal data set is 1 in 15,787.

Thus, in step 916, each of the outliers is weighted in accordance withthis approach. The weight to be applied to each execution outlier iscalculated as the absolute value of (amount above three standarddeviation limit/quality execution count standard deviation), with amaximum weight value being 2.0. The weight to be applied to each voltageoutlier is calculated as the absolute value of (amount above threestandard deviation limit/quality voltage standard deviation), with amaximum weight value being 2.0. The weight to be applied to each voltagevariance outlier is calculated as the absolute value of (amount abovethree standard deviation limit/quality voltage variance standarddeviation), with a maximum weight value being 2.0. The weight to beapplied to each amperage outlier is calculated as the absolute value of(amount above three standard deviation limit/quality amperage standarddeviation), with a maximum weight value being 2.0. The weight to beapplied to each amperage variance outlier is calculated as the absolutevalue of (amount above three standard deviation limit/quality amperagevariance standard deviation), with a maximum weight value being 2.0.

The second step (i.e., step 918) in weighting each of the outliers isdetermined by the execution count of the outlier's state. In particular,the value of each outlier is multiplied by the execution count of theoutlier's state, thereby accounting for the time contribution of thestate relative to the overall wave shape. In this manner, states thathave larger execution counts (i.e., execution times) produce outlierswith correspondingly heavier weighting. Consequently, as the executiontime for a particular outlier increases, the weight of the outlier willalso increase.

The weighting of the outliers, in steps 916 and 918, produce a set offinal weighted outliers including final weighted execution outliers,final weighted voltage outliers, final weighted voltage varianceoutliers, final weighted amperage outliers, and final weighted amperagevariance outliers. These final weighted outliers are summed in step 920to produce a final weighted outlier sum for each analysis packet.Thereafter, determination of a quality indicator for each of theanalysis packets is calculated, in step 922, as the quotient obtained bydividing a perfect quality value minus the final weighted outlier sum bythe perfect quality value. The perfect quality value is equal to theexecution count for the analysis packet multiplied by the number ofoutlier categories (i.e., in this case five).

Thus, an instantaneous quality indicator (i.e., for the currentcompleted analysis packet) can be determined during the welding processand communicated to the welder or otherwise utilized. In this manner,potential problems can be detected as they occur, i.e., during thewelding process, as opposed to only after the weld is complete, when itis likely too late to take any corrective action.

Furthermore, the average of the quality indicators aggregated up to anypoint of time during the welding process can be averaged to determine aquality indicator of the weld up to that point of time. For example,after the welding process is complete, all of the individual qualityindicators can be averaged to obtain an overall quality indicator,score, grade, rating or the like for the completed weld. The overallquality indicator for the weld can be compared against a predeterminedquality indicator (e.g., derived from a training weld) that reflects theminimum quality indicator value for an acceptable weld.

In this manner, a quality of a weld can be determine accurately,efficiently, consistently, and/or automatically, in real-time or nearreal-time. This is particularly advantageous since visible inspection ofa weld is not always sufficient to gauge its quality and since anoperator might not detect or otherwise appreciate deviations or otherproblems during the welding process that can affect overall weldquality.

In some exemplary embodiments, a quality indicator (i.e., a weld score)for a weld is an effective tool for evaluating welds being repetitivelyproduced under substantially the same conditions and according tosubstantially the same arc welding process, such as during an automated(e.g., robotic) welding process. By calculating instantaneous, periodic,and/or overall weld scores for each weld, an automated quality controlprocess can be adapted for the arc welding process. In particular, aminimum acceptable weld score or range of acceptable weld scores isinitially identified as a threshold, according to the weld conditionsand the arc welding process. Thereafter, each weld has its(instantaneous, periodic, and/or overall) weld score compared againstthe threshold to quickly and accurately determine whether the weldshould be accepted or rejected. Additionally, by evaluating trendsacross the weld scores for a production run or set of runs, problems inthe production process can be more readily identified, and/or theproduction process can be more readily optimized.

A conceptual production line 1000 is shown in FIG. 10, wherein a firstweld score S1 1002, a second weld score S2 1004, and a third weld scoreS3 1006 are associated with welds performed on a first workpiece WP11008, a second workpiece WP2 1010, and a third workpiece WP3 1012,respectively, by a welder or welding station 1014 including anintegrated monitor M 1016. One of ordinary skill in the art willappreciate that the different welds could be performed on the sameworkpiece.

The weld scores are then compared against a predetermined acceptableweld score threshold to determine whether each of the welds should beaccepted or rejected. This comparison can be done by the welder/weldingstation or by a separate device or at a separate location (e.g., anevaluation station 1018). In one exemplary embodiment, the comparisonbetween the weld score and the threshold is performed manually. In oneexemplary embodiment, an automated and manual comparison are performed.In one exemplary embodiment, the weld score is used to determine whethera manual inspection of the corresponding weld is warranted. In oneexemplary embodiment, the weld scores are used, at least in part, todetermine an overall efficiency of the production line.

In one exemplary embodiment, one or more evaluation stations 1018 aresituated along the production line 1000 to measure welds at specifiedstages of the production process. If an evaluation station 1018determines that a weld score for a weld meets or exceeds a predeterminedacceptable weld score threshold, the evaluation station 1018 accepts theweld by issuing an accept weld command 1020. In response to the acceptweld command 1020, the workpiece including the acceptable weld isallowed to continue along the production line 1000 for furtherprocessing.

Conversely, if the evaluation station 1018 determines that the weldscore for the weld falls below a predetermined acceptable weld scorethreshold, the evaluation station 1018 rejects the weld by issuing areject weld command 1022. In response to the reject weld command 1022,the workpiece including the unacceptable weld is routed off of theproduction line 1000 or otherwise removed from the production line 1000(e.g., manually removed). Thereafter, the workpiece having the rejectedweld can be subjected to further processing, for example, rehabilitatingor otherwise repairing the rejected weld, or recycling the workpieceentirely.

In one exemplary embodiment, each accept weld command 1020 and/or rejectweld command 1022 is logged or otherwise stored for later review andanalysis. In this manner, trends relating to the welding process and/orthe production process can be more readily identified, which in turn,may make it easier to increase the overall efficiency of the productionline utilizing the welding process.

In some exemplary embodiments, quality indicators (i.e., weld scores)computed for welds can be used in an innovative approach to providinginstruction or otherwise teaching an operator manually performing an arcwelding process. In particular, as the operator is using a welder (e.g.,the electric arc welder 10) to create the weld, instantaneous and/orperiodic weld scores are determined for the weld by the welder (e.g.,via a monitor M of the welder) and are used to provide direct feedbackto the operator relating to the current quality of the weld. As notedabove, these weld scores are based on weighted, statistical measurementsthat more accurately reflect weld quality as compared to a mere visualinspection of the weld. In particular, the weld scores are comparedagainst a predetermined acceptable weld score or range of acceptableweld scores to determine whether any corrective action is necessary bythe operator. Additionally, the weld scores are evaluated over time todetermine whether any trend in moving away from an acceptable weld score(e.g., as evidenced by a continuing reduction in the weld score) ispresent.

A method of instruction 1100, according to one exemplary embodiment, isshown in FIG. 11. The method 1100 begins with an operator starting toperform a welding process in step 1102.

During the welding process, a weld score is periodically calculated(based on one or more sampled or otherwise measured parameters), in step1104, to reflect a current status of the weld. The weld score can becalculated as an instantaneous measurement reflecting the current statusof the weld or as an average of several measurements reflecting thestatus of the weld over a period of time (corresponding to themeasurements) during the welding process. In one exemplary embodiment,the weld score is calculated by averaging all of the measurements takensince the welding process started, which reflects a current overallstatus of the weld.

Next, the weld score is compared to a predetermined threshold weld scorein step 1106. The threshold weld score is a minimum weld score for agood or otherwise acceptable weld status. If the weld score is greaterthan or equal to the threshold weld score, the current status of theweld is determined to be good in step 1108. Otherwise, the currentstatus of the weld is determined to be bad in step 1108.

If the current status of the weld is good, the operator is provided withan indication that the weld is good, in step 1110, which suggests thatthe welding process is being performed properly. Thereafter, the currentstatus of the weld is logged, in step 1112, for later review, analysis,and/or other use. The method of instruction 1100 then continues tomonitor the welding process being performed by the operator, asdescribed above.

If the current status of the weld is bad, the operator is provided withan indication that the weld is bad, in step 1114, which suggests thatthe welding process is being performed improperly. Thereafter, thecurrent status of the weld is logged, in step 1118, for later review,analysis, and/or other use. The method of instruction 1100 thencontinues to monitor the welding process being performed by theoperator, as described above.

The aforementioned indications can be provided to the operator in anymanner sufficient to inform the operator during the welding process. Inone exemplary embodiment, the indication is provided to the operatorvisually, such as on a display device integrated with or in closeproximity to the welder. In one exemplary embodiment, the instruction isvisually displayed on a protective visor or headgear worn by theoperator. In one exemplary embodiment, the instruction is provided tothe operator audibly, such as through a speaker integrated with or inclose proximity to the welder. In one exemplary embodiment, theinstruction is audibly played in protective headgear worn by theoperator.

In one exemplary embodiment, if the current status of the weld is bad,the operator receives instruction on what corrective action or actionsshould be taken in step 1116. In one exemplary embodiment, theinstruction is provided in real-time during the welding process. Theinstruction could, for example, involve a suggested change in a positionof an electrode (i.e., wire) relative to the workpiece or a suggestedchange in a rate of movement of the wire relative to the workpiece.

Various devices and techniques could be used to determine possiblecorrective actions to be taken, such as modeling operator and/or weldingconditions during a welding process that results in a verified good weldand using the resulting model data to evaluate other operators carryingout similar welding processes under similar conditions. Artificialintelligence and related simulations could also be used to build such amodel. Furthermore, sensors could be used to build such a model.

In one exemplary embodiment, one or more sensors are used to determineaspects of the welding process, for example, a current temperature ofthe workpiece, a level of shielding gas being delivered, and/or acomposition of the shielding gas. In one exemplary embodiment, one ormore sensors are used to determine environmental conditions that couldaffect the welding process, for example, wind conditions and/or humidityconditions. In one exemplary embodiment, one or more sensors are used todetermine operator conditions that could affect the welding process, forexample, the distance of the operator's hand from the workpiece and/orthe angle of the operator's hand from the workpiece. Data from these orother sensors is compared to model data to identify the instruction onwhat corrective action or actions should be taken by the operator.

In one exemplary embodiment, the corrective action instruction isprovided to the operator visually, such as on a display deviceintegrated with or in close proximity to the welder. In one exemplaryembodiment, the instruction is visually displayed on a protective visoror headgear worn by the operator. In one exemplary embodiment, theinstruction is provided to the operator audibly, such as through aspeaker integrated with or in close proximity to the welder. In oneexemplary embodiment, the instruction is audibly played in protectiveheadgear worn by the operator.

Thus, the method of instruction 1100 provides real-time feedback to theoperator during the welding process, such that the operator readilyknows when the weld is moving from a good condition toward a badcondition and when the weld is moving from a bad condition toward a goodcondition. Furthermore, the method of instruction 1100 can suggestcorrective action intended to improve the current (and thus overall)condition of the weld. As changes in the weld condition are oftenattributable to the actions of the operator, the feedback provided bythe method of instruction 1100 (including any suggested correctiveaction) teaches the operator good welding techniques. Furthermore, goodwelding techniques of the operator are reinforced by the continuedconfirmation of a good weld status.

The method of instruction 1100, or aspects thereof, can also readily beadapted or otherwise applied to a simulated welding process. In oneexemplary embodiment, the method of instruction 1100 is applied to awelding simulator utilizing virtual reality technology.

In some exemplary embodiments, a quality indicator (i.e., a weld score)computed for a weld performed by an operator can be used in aninnovative approach to certifying the operator with respect to aparticular welder, welding process, or welding course, similar to howgrades are used in general education. For example, the weld scores(e.g., an overall weld score) calculated in accordance with the methodof instruction 1100, or aspects thereof, provide a convenient platformfor certifying the operator. The operator must obtain a weld score orscores that exceed predefined threshold weld scores to be certified withrespect to the welder, welding process, or welding course. If theoperator fails to be certified, the method of instruction 1100 canidentify areas that need improvement to the operator. As describedherein, additional functionality (e.g., provided by software running inor external to the welder) can be used to measure other parameters thatmight be useful in certifying the operator. For example, the method ofinstruction 1100 could be modified to include tracking how much time theoperator spent actually welding during the welding process or course. Asanother example, the method of instruction 1100 could be modified toinclude tracking the amount of consumables (e.g., wire) used by theoperator during the welding process or course.

In addition to being used to certify an operator, the weld scores (andother parameters) can also be used to differentiate one operator fromanother. For example, notwithstanding that two operators both achievepassing scores and are certified with respect to a particular welder,welding process, or welding course, the scores of the two operatorsmight be vastly different. Accordingly, a certified operator with a muchhigher score could be chosen over another certified operation having alower score.

In some exemplary embodiments, quality indicators (i.e., weld scores)computed for welds and other related parameters and information can beused to assist an instructor teaching multiple students a weldingtechnique, process, program, course, or the like. A welding class oftenincludes a theoretical component and a practical component. Thetheoretical component is generally taught in the form of a lecture,discussion, or demonstration in a classroom or similar setting.Typically, a welding school or other environment for teaching studentsthe practical component of the class will include individual locationssuch as booths, similar to welding stations in a factory. Each studentis assigned to his or her own booth for performing the practicalcomponent of the course.

It is rather easy for the instructor to approximate how much time eachstudent spends on the theoretical component of the class, for example,by tracking each student's class attendance and/or participation duringdiscussions relating to the theoretical component. However, it isdifficult for the instructor to gauge how much time each studentactually spends on the practical component of the class because theinstructor cannot be at all of the booths all of the time. For example,the booths may be constructed and/or arranged such that the instructor'sline of sight only extends to a single booth at a time, i.e., the boothat which the instructor is currently present. The students at the otherbooths could be doing something other than welding (e.g., eating,sleeping, talking on the phone) without the instructor knowing. It isalso difficult for the instructor to readily determine which of thestudents would most likely benefit from the instructor's personalattention, at any given time. Thus, the instructor may wind up devotingtime to one student notwithstanding that another student has a greaterneed for the instructor's personal attention.

A system 1200 for monitoring students learning a welding technique,process, program, course, or the like, such as an arc welding process,according to one exemplary embodiment, is shown in FIG. 12. The system1200 includes an area of instruction 1202, such as a classroom or shop,in which eight booths 1204, 1206, 1208, 1210, 1212, 1214, 1216, and 1218are situated. Each of the booths includes a welder. In particular, afirst welder W1 1220 is located in the first booth 1204, a second welderW2 1222 is located in the second booth 1206, a third welder W3 1224 islocated in the third booth 1208, a fourth welder W4 1226 is located inthe fourth booth 1210, a fifth welder W5 1228 is located in the fifthbooth 1212, a sixth welder W6 1230 is located in the sixth booth 1214, aseventh welder W7 1232 is located in the seventh booth 1216, and aneighth welder W8 1234 is located in the eighth booth 1218. Furthermore,a student is assigned to each booth. In particular, a first student S11236 is assigned to work in the first booth 1204, a second student S21238 is assigned to work in the second booth 1206, a third student S31240 is assigned to work in the third booth 1208, a fourth student S41242 is assigned to work in the fourth booth 1210, a fifth student S51244 is assigned to work in the fifth booth 1212, a sixth student S61246 is assigned to work in the sixth booth 1214, a seventh student S71248 is assigned to work in the seventh booth 1216, and an eighthstudent S8 1250 is assigned to work in the eighth booth 1218.

The area of instruction 1202 is situated such that an instructor 1252can freely move from one booth to another to interact with the students.

In one exemplary embodiment, each of the welders W1, W2, W3, W4, W5, W6,W7, and W8 includes an integrated monitor M, like the welder 10 shown inFIG. 1. When a student is using a welder to create a weld, instantaneousand/or periodic weld scores are determined for the weld by the welder(via the monitor M) and are used to provide direct feedback to thestudent relating to the current quality of the weld. As describedherein, these weld scores are based on weighted, statisticalmeasurements that more accurately reflect weld quality as compared to amere visual inspection of the weld. In particular, the weld scores arecompared against a predetermined acceptable weld score or range ofacceptable weld scores (e.g., ascertained from a prior baseline weld) todetermine whether any corrective action is necessary by the student.Additionally, the weld scores are evaluated over time to determinewhether any trend in moving away from an acceptable weld score (e.g., asevidenced by a continuing reduction in the weld score) is present.

Each of the welders W1, W2, W3, W4, W5, W6, W7, and W8 is incommunication with a production monitoring system (PMS) 1254 over anetwork 1256. The network 1256 can be a wired or a wireless network. Inone exemplary embodiment, the network 1256 is an Ethernet network.

The PMS 1254 can be implemented using software, hardware, andcombinations thereof, without departing from the spirit and the scope ofthe general inventive concepts. In one exemplary embodiment, the PMS1254 is implemented as software running on a general purpose computer(e.g., a PC) with peripherals such as a display device 1258 and a datastore 1260 connected thereto. In one exemplary embodiment, the PMS 1254could include logic integrated with each of the welders, as in the caseof the monitors M. As noted above, the PMS 1254 is in data communicationwith the welders W1, W2, W3, W4, W5, W6, W7, and W8 over the network1256.

The PMS 1254 is a weld data collection and monitoring tool that isoperable, for example, to collect short-term and long-term welding logscomplete with statistics for each recorded weld. The PMS 1254 can alsotrack other production related parameters and conditions, such as wireconsumption. In the system 1200, the PMS 1254 collects data from each ofthe welders W1, W2, W3, W4, W5, W6, W7, and W8 to determine an amount oftime spent by the respective students S1, S2, S3, S4, S5, S6, S7, and S8in creating the weld. The amount of time spent by each of the studentsS1, S2, S3, S4, S5, S6, S7, and S8 (i.e., the welding times) can besaved by the PMS 1254 to the data store 1260 for later retrieval anduse. Additionally, the PMS 1254 receives the weld scores from each ofthe welders W1, W2, W3, W4, W5, W6, W7, and W8 over the network 1256,which can then be saved by the PMS 1254 to the data store 1260 for laterretrieval and use. Thus, the PMS 1254 is capable of generating andstoring logs of welding times and weld scores for multiple students overmultiple evaluation periods, which can be a tremendous resource for theinstructor 1252 in teaching and assessing the students.

Additionally, the PMS 1254 can display, in real time, the currentwelding times for each of the students S1, S2, S3, S4, S5, S6, S7, andS8, in combination with the current weld scores for each of the studentsS1, S2, S3, S4, S5, S6, S7, and S8, on the display device 1258. In thismanner, the instructor 1252, by observing the display device 1258, canget an instant and accurate assessment of the current status of each ofthe students and their respective welds. This allows the instructor 1252to better proportion his or her time in relation to those studentsexhibiting the greatest need.

In the system 1200, the welding times and weld scores can be displayedin any manner, for example, as numerical data and/or as graphical data.In one exemplary embodiment, the PMS 1254 provides a web-based userinterface that supports accessing data, viewing data, generatingreports, etc. via a web browser.

The system 1200 is readily scalable to accommodate any number ofstudents, as well as multiple instructors.

A method 1300 of monitoring students learning a welding technique,process, program, course, or the like, such as an arc welding process,according to one exemplary embodiment, is shown in FIG. 13. The method1300 involves multiple students performing the arc welding process instep 1302. In one exemplary embodiment, the students performsubstantially the same arc welding process under substantially the sameconditions and at substantially the same time.

During the arc welding process, a weld score is periodically calculated(based on one or more sampled or otherwise measured parameters) for eachstudent, in step 1304, to reflect a current status of the student'sweld. The weld score can be calculated as an instantaneous measurementreflecting the current status of the student's weld or as an average ofseveral measurements reflecting the status of the student's weld over aperiod of time (corresponding to the measurements) during the arcwelding process. In one exemplary embodiment, the students weld score iscalculated by averaging all of the measurements taken since the arcwelding process started, which reflects a current overall status of thestudent's weld.

During the evaluation period of the method 1300, an amount of time theeach student spends performing the arc welding process (i.e., actuallywelding) is determined in step 1306. Operational data collected from thewelder of each student can be used to determine the students' weldingtimes.

Each weld score is associated with its corresponding student in step1308. Similarly, each welding time is associated with its correspondingstudent in step 1308. Identifying information (e.g., a serial number)from the welder assigned to each student can be used to associate datacollected from and/or generated by the welders (e.g., the weld score,the welding time) with the respective students.

Once the weld scores and welding times are associated with therespective students, this information can be output in any manner instep 1310. For example, a report of all of the students and theirrespective weld scores and welding times can be output to a displaydevice, such as a monitor. As another example, information on thestudents and their respective weld scores and welding times can belogged and stored in a data store, such as a disk drive or flash drive,for later retrieval and use. In one exemplary embodiment, theinformation is output periodically. In one exemplary embodiment, theinformation is output at the end of the evaluation period.

The weld scores and/or the welding times can also be used to generateadditional identifying information for the students. For example, theweld score and/or the welding time for a student can be compared againstpredetermined thresholds. In this manner, based on the weld score and/orthe welding time for a student, a pass or a fail determination can bedetermined for the weld of the student.

In some exemplary embodiments, weld scores computed for welds can beused in an innovative approach to identifying potential cost savings fora welding process. In one exemplary embodiment, a cost analysis (e.g.,cost-effective analysis, cost-benefit analysis) is performed for awelding process based on a series of welds performed according to thewelding process. Data 1400 corresponding to exemplary welds, as shown inFIGS. 14A-148, can be used in performing the cost analysis.

First, a plurality of weld conditions 1402 that affect overall weldquality are selected. For example, in FIGS. 14A and 148, the weldconditions 1402 include wire characteristics (e.g., wire composition1404, wire diameter, coating), workpiece characteristics (e.g.,workpiece composition 1406, workpiece thickness), a shielding gas flowrate 1408, a shielding gas composition 1410, and/or a workpiece pre-heattemperature 1412. Next, one of these weld conditions 1402 is varied, asindicated at 1414, across the series of welds, while all of theremaining weld conditions 1402 are fixed, as indicated at 1414, acrossthe series of welds.

For each of the welds in the series, a weld score 1416 is alsocalculated based on the current weld conditions 1402, 1414. The weldscore 1416 represents a measure of the overall quality of the weldcreated under the weld conditions. As noted above, these weld scores arebased on weighted, statistical measurements that more accurately reflectweld quality as compared to a mere visual inspection of the weld.

Additionally, for each of the welds in the series, a cost for creatingthe weld is determined. In one exemplary embodiment, the cost includesmonetary expenditures related to producing the weld, represented as amonetary cost 1418 for the weld. In one exemplary embodiment, the costincludes a total time required to complete the weld, represented as atime cost 1420 for the weld. Each weld in the series is associated withits corresponding weld score and cost.

FIGS. 14A and 148 include, respectively, data 1400 for two welds in aseries of welds wherein among the weld conditions 1402, the wirecomposition 1404, the workpiece composition 1406, the shielding gascomposition 1410, and the workpiece pre-heat temperature 1412 are fixed,as shown at 1414, across the series of welds, while the shielding gasflow rate 1408 is varied (e.g., incrementally increased or decreased),as shown at 1414, across the series of welds.

For the weld corresponding to FIG. 14A, a monetary cost 1418 of a, atime cost 1420 of b, and a weld score 1416 of c are calculated orotherwise determined. For the weld corresponding to FIG. 148, a monetarycost 1418 of d, a time cost 1420 of e, and a weld score 1416 of f arecalculated or otherwise determined. Thus, if it is determined that a<d,b<e, and c=f, it can be deduced that the shielding gas flow rate 1408 ofFIG. 14A is superior to the shielding gas flow rate 1408 of FIG. 148,since both a cost and time savings are realized without any reduction inoverall weld quality by the shielding gas flow rate 1408 of FIG. 14A ascompared to the shielding gas flow rate 1408 of FIG. 148. If it isinstead determined that a<d, b>>e, and c=f, it can be deduced that theshielding gas flow rate 1408 of FIG. 14A provides a cost savings withoutany reduction in overall weld quality, but at a substantially increasedtime cost, as compared to the shielding gas flow rate 1408 of FIG. 148.

In this manner, a user will be able to readily identify the impact thevaried weld condition has on overall weld quality in the series and,thus, in the corresponding welding process. In this manner, the user candetermine whether varying the weld condition (and in what manner) willallow the user to obtain a more desired weld quality, a more desiredcost, or both. Consequently, as more welds are performed and thecorresponding data analyzed, the impact of any one or more weldconditions on the overall welding process can be readily determined andevaluated, such that more informed cost saving decisions (e.g., relativeto money, time, and quality tradeoffs) can be made.

The cost analysis could be expanded to include additional series ofwelds, wherein different weld conditions are varied in the differentseries. In this manner, the user can identify a desired value or settingfor a plurality of the weld conditions to achieve a desired outcome(e.g., acceptable weld quality and acceptable cost). These desiredvalues or settings for the weld conditions could then be saved in aprofile associated with the welder and the welding process forsubsequent retrieval and use for the same welder and welding process,thereby increasing the likelihood that the user will again achieve thedesired outcome.

In one exemplary embodiment, a plurality of such profiles (i.e., sets ofselected weld parameters and/or weld conditions) are saved, i.e., aspre-sets, such that the profiles can be accessed by a user beginning awelding process. In one exemplary embodiment, a plurality of pre-setsare presented to a user along with a weld score corresponding to eachpre-set. Each weld score quantifies an overall quality of a weldpreviously obtained using the weld parameters and weld conditionsassociated with the pre-set. As noted above, these weld scores are basedon weighted, statistical measurements that more accurately reflect weldquality as compared to a mere visual inspection of the weld. The usercan then select one of the pre-sets for performing the welding process,thereby increasing the likelihood that the user will achieve the same ora substantially similar weld as that previously produced using the weldparameters and weld conditions associated with the pre-set. In oneexemplary embodiment, a user interface is provided to allow the user tofilter out pre-sets that do not match criteria input by the user, forexample, filtering out those pre-sets that have an associated weld scorebelow an input threshold.

FIG. 15 shows pre-sets 1500, according to one exemplary embodiment. Eachof the pre-sets 1500 includes an identifying pre-set number 1502, a setof weld conditions 1504, welder information 1506, welding processinformation 1508, a monetary cost 1510, a time cost 1512, and anassociated weld score 1514. A first pre-set 1516, having pre-set number01, is associated with weld conditions 1504 having values a, b, c, d,and e and a welder M. The first pre-set 1516 corresponds to a weldingprocess O. If a user selects the first pre-set 1516 (i.e., pre-set 01)for performing the welding process O with the welder M under the weldingconditions a, b, c, d, and e, the user can expect a weld resulting fromthe welding process O to have a monetary cost of approximately t, a timecost of approximately v, and a weld score of approximately x. Thepre-sets 1500 can include additional pre-sets, such as a second pre-set1518, associated with different combinations of weld conditions 1504,welders 1506, and/or welding processes 1508.

In addition to the monitored weld parameters already described herein,additional weld parameters may be monitored for one or more states of awave shape, and additional quality parameters may be calculatedtherefrom to more accurately detect and identify weld defects.Additional weld parameters may include a welding torch or gun position,a level of sound produced by the arc welding process, at least onefrequency of sound produced by the arc welding process, and a pulsingrate of sound produced by the arc welding process. More additionalwelding parameters include a level of visible light produced by the arcwelding process, at least one frequency of visible light produced by thearc welding process, and a pulsing rate of visible light produced by thearc welding process. Further additional welding parameters include alevel of infrared light produced by the arc welding process, at leastone frequency of infrared light produced by the arc welding process, apulsing rate of infrared light produced by the arc welding process, anda wire feed motor current level.

In accordance with an embodiment, the additional weld parameters aresensed by sensors or monitoring devices that are appropriate fordetecting such weld parameters. For example, sound may be sensed by amicrophone, visible light may be sensed by a photo-detector, infraredlight may be sensed by an infrared detector, wire feed motor current maybe sensed by a current shunt. Torch position may be sensed using one ormore types of sensing technologies including, for example, imagingsensors or magnetic sensors. The sensors may be located in variousplaces including, for example, on a welding torch, on a welding helmet,or in the general welding area. The additionally sensed weld parametersmay be input to and processed by the monitor M, in a manner similar tohow the other weld parameters are input and processed as describedpreviously herein. Other weld parameters that may be monitored anprocessed include a temperature of the workpiece, a level of a shieldinggas, a composition of a shielding gas, a wind speed near the workpiece,a humidity level near the workpiece, and an operator position.

The monitor M may be configured (e.g., as upgraded monitor M′ shown inFIG. 16) to calculate a plurality of quality parameter statistics basedon the additionally monitored weld parameters. The various qualityparameter statistics of “average”, “standard deviation”, “varianceaverage”, and “variance standard deviation” may be calculated for thevarious additional weld parameters for one or more states of a waveshape in a manner similar to how those quality parameter statistics arecalculated for voltage and current as described previously herein. Thatis, quality parameters based on the additional monitored weld parametersmay be calculated in a manner similar to how QVA, AVSD, QVVA, and QVVSDare calculated for monitored voltage, and how QIA, QISD, QIVA, and QIVSDare calculated for monitored current.

For example, the monitor M may be configured to calculate sound levelquality parameters during the welding process for one or more statesover a period of time such as a quality sound level average (QSLA), aquality sound level standard deviation (QSLSD), a quality sound levelvariance average (QSLVA), and a quality sound level variance standarddeviation (QSLVSD). The monitor M may also be configured to calculatesound frequency quality parameters for one or more states over a periodof time such as a quality sound frequency average (QSFA), a qualitysound frequency standard deviation (QSFSD), a quality sound frequencyvariance average (QSFVA), and a quality sound frequency variancestandard deviation (QSFVSD). Furthermore, the monitor M may also beconfigured to calculate sound pulse rate quality parameters for one ormore states over a period of time such as a quality sound pulse rateaverage (QSPRA), a quality sound pulse rate standard deviation (QSPRSD),a quality sound pulse rate variance average (QSPRVA), and a qualitysound pulse rate variance standard deviation (QSPRVSD). The sound of thewelding process is produced by the arc between the wire electrode andthe workpiece. Specific sound characteristics tend to occur duringspecific states of the welding wave shape.

The monitor M may be configured to calculate visible light level qualityparameters during the welding process for one or more states over aperiod of time such as a quality visible light level average (QVLLA), aquality visible light level standard deviation (QVLLSD), a qualityvisible light level variance average (QVLLVA), and a quality visiblelight level variance standard deviation (QVLLVSD). The monitor M mayalso be configured to calculate visible light frequency qualityparameters for one or more states over a period of time such as aquality visible light frequency average (QVLFA), a quality visible lightfrequency standard deviation (QVLFSD), a quality visible light frequencyvariance average (QVLFVA), and a quality visible light frequencyvariance standard deviation (QVLFVSD). Furthermore, the monitor M mayalso be configured to calculate visible light pulse rate qualityparameters for one or more states over a period of time such as aquality visible light pulse rate average (QVLPRA), a quality visiblelight pulse rate standard deviation (QVLPRSD), a quality visible lightpulse rate variance average (QVLPRVA), and a quality visible light pulserate variance standard deviation (QVLPRVSD). The visible light of thewelding process is produced by the arc between the wire electrode andthe workpiece.

The monitor M may be configured to calculate infrared light levelquality parameters during the welding process for one or more statesover a period of time such as a quality infrared light level average(QIRLLA), a quality infrared light level standard deviation (QIRLLSD), aquality infrared light level variance average (QIRLLVA), and a qualityinfrared light level variance standard deviation (QIRLLVSD). The monitorM may also be configured to calculate infrared light frequency qualityparameters for one or more states over a period of time such as aquality infrared light frequency average (QIRLFA), a quality infraredlight frequency standard deviation (QIRLFSD), a quality infrared lightfrequency variance average (QIRLFVA), and a quality infrared lightfrequency variance standard deviation (QIRLFVSD). Furthermore, themonitor M may also be configured to calculate infrared light pulse ratequality parameters for one or more states over a period of time such asa quality infrared light pulse rate average (QIRLPRA), a qualityinfrared light pulse rate standard deviation (QIRLPRSD), a qualityinfrared light pulse rate variance average (QIRLPRVA), and a qualityinfrared light pulse rate variance standard deviation (QIRLPRVSD). Theinfrared light of the welding process is produced by the arc between thewire electrode and the workpiece.

The monitor M may be configured to calculate wire feed motor currentquality parameters during the welding process for one or more statesover a period of time such as a quality wire feed motor current average(QWFMIA), a quality wire feed motor current standard deviation(QWFMISD), a quality wire feed motor current variance average (QWFMIVA),and a quality wire feed motor current variance standard deviation(QWFMIVSD). The wire feed motor current is produced by the motor of thewire feeder during the welding process. When a contact tip becomes worn,or a wrong contact tip is being used, shifts or spikes in the motorcurrent may be observed during certain states.

Again the various quality parameter statistics of “average”, “standarddeviation”, “variance average”, and “variance standard deviation” may becalculated for the various additional weld parameters in a mannersimilar to how those quality parameter statistics are calculated forvoltage and current as described previously herein. Furthermore, inaccordance with an embodiment, a value of each of the calculated qualityparameters for each period of time may be compared to a correspondingexpected quality parameter value to determine if a difference betweenthe calculated quality parameter value and the expected qualityparameter value exceeds a predetermined threshold. If the differenceexceeds the threshold, the calculated quality parameter value may beweighted with a magnitude weight based on the difference, and/orweighted with a time contribution weight based on a time contribution ofits state to the wave shape including the state.

In accordance with an embodiment of the present invention, the qualityparameters (weighted or unweighted) and/or the weld parameters may beused to diagnose the arc welding process. FIG. 16 illustrates aschematic block diagram of an embodiment of a system 1600 for diagnosingan arc welding process. The system 1600 corresponds to a portion of anarc welding system and includes an upgraded monitor M′ 1610 which issimilar to the monitor M but is configured to further monitor theadditional weld parameters discussed herein and further calculate thecorresponding additional quality parameters. The system 1600 alsoincludes a diagnostic logic circuit (DLC) 1620 in operativecommunication with the upgraded monitor M′ 1610.

As illustrated in FIG. 16, the calculated quality parameters and/or themonitored weld parameters, or some subset thereof, is passed to the DLC1620 which operates on the parameters to generate diagnostic results. Inaccordance with an embodiment, the DLC 1620 first identifies localizedor continuous defects of the weld by analyzing the quality parameters. Alocalized defect is a defect that occurs over a relatively short periodof time during the welding process (e.g., 2 seconds). A continuousdefect is a defect that occurs over essentially the entire time of thewelding process (e.g., 20 seconds). Some examples of defects include gasinclusions in the weld (e.g., porosity, blow holes, worm holes),burnthrough of the workpiece, lack of penetration into the workpiece,splatter, an underfilled joint, undercut, cracking of the weld, voids inthe weld, and lack of fusion. Such types of defects are well known inthe art. Other types of defects may be possible as well.

Some examples of possible causes of defects include a lack of shieldinggas, a short contact tip to work distance, a long contact tip to workdistance, a clogged nozzle, workpiece surface contamination, too slow atravel speed, too fast a travel speed, too slow a wire feed speed, toofast a wire feed speed, sulfur content in the workpiece or electrode,excessive moisture from the electrode or workpiece, and too small of anelectrode angle. Other types of causes of defects are possible as well.

During training weld procedures, defects are correlated to causes of thedefects and the DLC 1620 is programmed accordingly to properly associatedefects with one or more possible causes. Therefore, during a normal(non-training) welding procedure, the DLC 1620 is able to suggest one ormore possible causes of one or more detected defects. The defects andthe one or more causes may be reported to the operator, allowing theoperator to correct the problem. The DLC 1620 may be programmed as adecision tree, for example, to isolate to a cause of a defect.

As an example, the system 1600 may detect porosity, occurring in certainstates, and splatter, occurring in certain other states, as twocontinuous defects occurring during a welding process by analyzing thequality parameters. The DLC 1620 may correlate the occurrence of the twocontinuous defects in the respective states to a lack of shielding gasthroughout the welding process. The operator may subsequently find thatthe valve of the gas tank was turned off during the welding process.

FIG. 17 is a flowchart of a method 1700 of diagnosing an arc weldingprocess using the system 1600 of FIG. 16 by monitoring an electric arcwelder as the welder performs the arc welding process by creating actualwelding parameters between an advancing wire and a workpiece to create aweld. The welding process is controlled by command signals to a powersupply of the welder. In step 1710 of the method 1700, a series ofrapidly repeating wave shapes is generated, each wave shape constitutinga weld cycle with a cycle time. In step 1720, the wave shapes aredivided into states. In step 1730, a plurality of weld parametersoccurring in one or more of the states are measured at an interrogationrate over a period of time repeatedly during the welding process.

In step 1740 of the method 1700, a plurality of quality parameters arecalculated for each of the states based on the measurements of the weldparameters during the welding process. In step 1750, at least one of theplurality of quality parameters and the plurality of weld parameters areanalyzed to diagnose the arc welding process by determining one or morepossible causes of one or more localized or continuous defects of theweld.

In summary, an arc welding system and methods are disclosed. The systemis capable of monitoring variables during a welding process, accordingto wave shape states, and weighting the variables accordingly, detectingdefects of a weld, diagnosing possible causes of the defects,quantifying overall quality of a weld, obtaining and using dataindicative of a good weld, improving production and quality control foran automated welding process, teaching proper welding techniques,identifying cost savings for a welding process, and deriving optimalwelding settings to be used as pre-sets for different welding processesor applications.

In the foregoing embodiments, weld parameters are measured or monitoredduring a welding process to quantify a weld quality. The weld parametersare input to the monitor M (or upgraded monitor M′), which outputs oneor more quality parameters, one or more quality indicators (e.g., weldscores), or an accept/reject indicator as described above. It is to beappreciated that the monitor M (or more specifically the circuit, logic,software, etc implementing the monitor) can perform the single task ofoutputting the one or more quality parameters while other, related, andconnected circuits or software can generate quality indicators oraccept/reject indicators. These separate functional elements, which canbe implemented as circuits, logical elements, software executing on acomputer processor, etc., are combinable into larger functional blocksand/or systems. Accordingly, the descriptions of such functional aspectsare organized herein for convenience and it is to be appreciated thatany combination of such aspects in physical systems are intended to bewithin the scope of the claimed subject matter.

The quality parameters generated from monitoring a baseline or trainingwelding process can be utilized to evaluate other welding processes.These quality parameters can form the basis of baseline qualityindicators, thresholds, or other metrics used to evaluate other welds,as described above. Whether for a training weld or an evaluation weld(i.e., a weld to be measured against the training weld), a welderperforms a welding process by creating welding parameters between anadvancing wire and a workpiece. The welding process is controlled withcommand signals to a power supply of the welder, a motor driving thewire feed, etc. For example, the command signals can be transmitted bywave shape generator 40 from FIG. 1 or a controller of the welder, suchas controller 1810 described below. The welding parameters are generatedaccording to a waveform, which can include a series of repeating waveshapes, each of which constitutes a weld cycle with a cycle time. Eachwave shape can be segmented into states, which as described above, canbe monitored independently or collectively to determine qualityparameters for individual states, complete weld cycles, or the entirewelding process (i.e., the series of repeating wave shapes). The qualityparameters of a monitored welding process can be compared to referenceor trained quality parameters to evaluate an overall quality of a weldas acceptable or unacceptable. For instance, in one aspect, the trainedquality parameters can indicate minimum thresholds to qualify as anacceptable weld. It is to be appreciated that such monitoring andevaluation can occur at the various levels described above. That is,monitoring and evaluation can be performed on a granular level (i.e.,over a sampling period), over one state of one wave shape, over theentire wave shape, or over the entire series of wave shapes (e.g.,welding process).

In forgoing embodiments, such comparisons are utilized with weldingprocesses generally performed under substantially similar weldingconditions as the training weld. Welding conditions such as, but notlimited to, contact tip-to-work distance, weld gap, and weld speed (i.e.transit speed) can vary between from welding process to welding processand, further, can vary between successive runs of the same weldingprocess. In addition, the varying welding conditions can affectgenerated welding parameters. For instance, an arc welder generates awaveform, in the form of command signals for example, to control a powersupply of the welder. The waveform is generally configured to regulate aconstant output voltage between the wire and the workpiece. However, inresponse to varying welding conditions, a controller of the welder canalter the waveform to adjust. According to one example, the controlleradjusts the waveform (i.e., the command signals) to maintain a constantoutput voltage.

Varying and differing welding conditions, and subsequent changesimplemented by the controller, affect welding parameters generatedbetween the wire and workpiece. Accordingly, when monitoring the weldingparameters and determining corresponding quality parameters, noise isintroduced both in the welding parameters and in subsequently determinedquality parameters and indicators. Such noise impacts reliability ofquality indicators and comparisons with training welds performed underdifferent welding conditions.

According to an aspect, non-adaptive portions or states of the waveformcan be leveraged to improve monitor performance. That is, weldingparameters can be effectively monitored and weld quality can bedetermined even for welding processes performed in welding conditionsdiffering from the trained conditions or performed under varying weldingconditions. Non-adaptive portions of the waveform refer to portions,states, or segments of the waveform for which the command signals do notchange with differences in welding conditions. That is, despitedifferent or varying welding conditions, the controller of the welderdoes not alter the command signal issued to the power supply of thewelder the in non-adaptive segments. According to a further example, thenon-adaptive segment can refer to portions of the welding process inwhich adaptive control is not performed. Here, adaptive control refersto the characteristic of the controller to change the waveform toregulate the constant output voltage in response to varying conditions.

In accordance with an embodiment, non-adaptive segments or states of thewave shape are selected for monitoring. Accordingly, variations inwelding parameters due to controller adaptations are not encountered.According to another embodiment, non-adaptive segments are evaluated todetermine differences in welding conditions between normal(non-training) welding processes and training welding processes. Theresultant effects on monitored welding parameters and/or qualityparameters can be identified and utilized to remove noise in the modelof the welding process. Accordingly, the normal welding processes can beevaluated against the training welding processes despite differentwelding conditions.

FIG. 18 illustrates a schematic block diagram of a system 1800 foradjusting welding process monitoring and evaluation in response todifferent or varying welding conditions. The system 1800 corresponds toa portion of an arc welding system that includes a controller 1810 and apower supply 1820, and a wire feeder 1830. The system 1800 furtherincludes an monitor 1840 that determines quality parameters based onwelding parameters.

Controller 1810 implements a welding process by controller the powersupply 1820 and the wire feeder 1830. Controller 1810 generates awaveform and issues command signals, in accordance with the waveform, tothe power supply 1820 and the wire feeder 1830. The power supply 1820and the wire feeder 1830 convert the command signals into suitableoutputs to enact the welding process. Moreover, controller 1810 can beconfigured to provide adaptive control of the welding process. That is,the controller 1810 adjusts the waveform, and accordingly the commandsignals, in response to welding conditions. Some welding conditions canbe measured or sensed directly, with sensors for example, as discussedabove. Other differences or changes in welding conditions are indirectlyidentified by, for example, detecting changes in measured weldingparameters (e.g., output voltage, etc.). As shown in FIG. 18, thewelding parameters and/or welding conditions monitored, sensed, ormeasured as described in previous embodiment are input to the controller1810 to enable adaptive control.

To facilitate determination of quality parameters, monitor 1840communicates with controller 1810 to ascertain a current portion orstate of the generated waveform, to acquire command signals output bycontroller 1810, and, in the case of adaptive control, to identify whenadaptive control is being applied and to what extent. For instance, tofacilitate operation of the monitor 1840, the controller 1810 cantemporarily disable adaptive control to remove the effects of suchcontrol in the welding parameters measured by the monitor 1840. Turningto FIG. 19, a schematic block diagram of an exemplary, non-limitingembodiment for monitor 1840 is illustrated. As depicted, monitor 1840includes estimation logic 1842 configured to identify an effect ofdifferent or varying welding conditions, under which a welding processis performed, on monitored welding parameters or sensed weldingconditions. In an example, estimation logic 1842 outputs noiseparameters or a noise signal representing the effect of changingconditions on welding parameters. For instance, estimation logic 1842can obtain, as input, controller information from controller 1810. Thecontroller information can include, but is not limited to, commandsignals, waveform states or segments, signals indicative of adaptivechanges implemented by the controller 1810, a signal indicative that thecontroller 1810 is outputting a non-adaptive portion of the waveform,and/or a signal enabling/disabling adaptive control. Non-adaptiveportions of the waveform can include peak current segments or backgroundcurrent frequency segments. However, it is to be appreciated that otherstates or portions can be made or designated as non-adaptive segments.

During a non-adaptive segment of the waveform, estimation logic 1842 canidentify deviations, changes, or fluctuations in welding parametersmonitored as described previously. In one example, the estimation logic1842 can utilize techniques similar to those employed by level monitorstage 81 and/or stability monitor stage 91 (described above) to identifydifferences between a measured welding parameter and a command signal.Alternatively, the estimation logic 1842 can compare welding parametersmonitored over the non-adaptive segment against welding parametersrecorded for a corresponding segment of a training welding process.Further, in both examples, the estimation logic 1842 can utilize weldingconditions sensed, as described in previous embodiments, to determinewhether the normal welding process is being performed under differentwelding conditions from the training welding process. Based on thisdetermination, the estimation logic 1842 can generate a model of theeffect of the differing welding conditions on the monitored weldingparameters. This model, in turn, be utilized by the estimation logic1842 to output the noise parameters or noise signal. According toanother example, the estimation logic 1842 can output the noiseparameters or noise signal based on the identified deviations betweenthe measured welding parameters and the command signals. In other words,once determined that the welding process is performed under differentconditions than the training weld, the estimation logic 1842 canattribute such deviations in the welding parameters to those changedconditions and, thus, generate the noise parameters representing themagnitude of the deviations and/or the noise signal as a differencesignal between the measured parameters and the command signals.

It is to be appreciated that the estimation logic 1842 can construct ormodel noise parameters or signals individually for each weldingparameter that is monitored. Accordingly, each welding parameter can beadjusted separately in order to monitor and evaluate welding processesdespite changing welding conditions. In one aspect, as shown in FIG. 19,monitor 1840 can include filter logic 1844 configured to adjust weldingparameters monitored in accordance with the noise parameters or signalsprovided by the estimation logic 1842. The welding parameters adjustedby filter logic 1844 can be associated with any monitored portion orsegment of the waveform, whether adaptive or non-adaptive. Further tothis aspect, the filter logic 1844 outputs adjusted or scrubbed weldingparameters that account for changed welding conditions which can besubstantially overcome with utilization of adaptive control.Accordingly, the adjusted welding parameters facilitate determination ofweld quality using techniques described above, since deviations inwelding parameters, due to varying conditions and/or adaptive control,are isolated. For instance, the adjusted welding parameters can be inputto weld evaluation logic 1846, which can be substantially similar tomonitor M or monitor M′ described previously. That is, the weldevaluation logic 1846 can monitor or measure the adjusted weldingparameters and calculate corresponding quality parameters and/or qualityindicators. Moreover, the weld evaluation logic 1846 can comparecalculated quality parameters and/or quality indicators with trainingdata.

According to another aspect, the estimation logic 1842 can generate anadaptation model based on the noise parameters and knowledge of theadaptability algorithms employed by controller 1810. The adaptationmodel can be utilized to render appropriate adjustments to monitoringwelding parameters. For instance, thresholds or other characteristics ofweld evaluation logic 1846, utilized to monitor parameters and calculatequality metrics as described above, can be configured based on theadaptation model to account for welding parameter values altered becauseof adaptive control.

FIG. 20 illustrates a flowchart of a method for determining a quality ofa weld by monitoring a welder as the welder performs a welding processby creating actual welding parameters between an advancing wire and aworkpiece. The method depicted in FIG. 20 can be performed by system1800, for example. At step 2000, welding parameters are acquired (i.e.,monitored or measured) during a non-adaptive segment of a waveform. Atstep 2002, noise in the welding parameters due to different weldingconditions is identified. At step 2004, adjustments to measured weldingparameters or how welding parameters are measured are determined toaccount for the noise identified. For example, measured weldingparameters from adaptive and non-adaptive portions of the waveform arefiltered to remove the noise. In another example, the metrics used tomeasure welding parameters from adaptive and non-adaptive portions ofthe waveform are configured to account for command signals adaptivelyapplied for changing welding conditions. At step 2006, weld quality isdetermined based on adjusted welding parameters.

In summary, an arc welding system and methods are disclosed. Forexample, in one embodiment, a method of determining a quality of a weldby monitoring a welder as the welder performs a welding process bycreating actual welding parameters between an advancing wire and aworkpiece is provided. The welding process is defined by a series ofrapidly repeating wave shapes controlled by command signals to a powersupply of the welder. The method includes segmenting a wave shape,having a weld cycle with a cycle time, into a series of time-segmentedstates. The method also includes selecting a non-adaptive state from theseries of time-segmented states. The non-adaptive state represents asegment of the wave shape where the command signals remain invariableunder different weld conditions. The method further include measuring aplurality of weld parameters generated between the advancing wire andthe workpiece during the non-adaptive state at an interrogation rateover an interval of time. In addition, the method includes calculating aplurality of quality parameters for the non-adaptive state based onmeasurements of the plurality of weld parameters acquired during theinterval of time within the non-adaptive state.

According to an example, the method can further include measuring theplurality of weld parameters occurring in the non-adaptive staterepeatedly at the interrogation rate for a plurality of intervals oftime; and repeatedly calculating the plurality of quality parameters forrespective measurements acquired for the plurality of intervals of time.

According to another example, for a quality parameter of the pluralityof quality parameters, the method can include comparing a value of eachof the quality parameter calculated for the interval of time to acorresponding expected quality parameter value to determine if adifference between the value of the quality parameter calculated and theexpected quality parameter value exceeds a predetermined threshold. Whenthe difference exceeds the predetermined threshold, the method caninclude weighting the value of the quality parameter calculated with amagnitude weight based on the difference, and weighting the value of thequality parameter calculated with a time contribution weight based on atime contribution of the interval of time to the wave shape includingthe interval of time. The method can also include calculating a qualityindicator for the interval of time based on weighted values of thequality parameters. Further still, the method can include aggregating aplurality of quality indicators respectively associated with a pluralityof intervals of time; and calculating an overall quality indicator forthe welding process based on the plurality of quality indicatorsaggregated.

According to another aspect, the method can include utilizing theplurality of quality parameters calculated for the non-adaptive state astraining data to evaluate subsequent welding processes.

According to yet another aspect, the method can include monitoringvalues for the command signals during the non-adaptive state at theinterrogation rate repeatedly over a plurality of intervals of time.Further to this aspect, the method includes calculating noise parametersbased, at least in part, on differences between the values of thecommand signals monitored and the measurements of the plurality of weldparameters acquired during the plurality of intervals of time.

In addition, the method can include measuring the plurality of weldparameters generated between the advancing wire and the workpiece duringone or more adaptive states from the series of time-segmented states;and adjusting the plurality of weld parameters measured in the one ormore adaptive states based on the noise parameters calculated based onthe non-adaptive state. Further, the method can include calculating theplurality of quality parameters for the one or more adaptive statesbased on adjusted measurements of the plurality of weld parametersacquired during the one or more adaptive states.

Alternatively, the method can include calculating an adaptation modelbased on the noise parameters, wherein the adaptation model representschanges to command signals by an adaptive controller of the welder toaccount for weld conditions. Further to this example, the method caninclude measuring the plurality of weld parameters generated between theadvancing wire and the workpiece during one or more adaptive states fromthe series of time-segmented states; and adjusting the plurality of weldparameters measured in the one or more adaptive states based on theadaptation model.

According to another example, a system for determining a quality of aweld by monitoring a welder as the welder performs a welding process bycreating actual welding parameters between an advancing wire and aworkpiece. The welding process being defined by a series of rapidlyrepeating wave shapes controlled by command signals to a power supply ofthe welder. The system includes a logic state controller for segmentinga wave shape, having a weld cycle with a cycle time, into a series oftime-segmented states. The system further includes a selection circuitfor selecting a non-adaptive state from the series of time-segmentedstates. The non-adaptive state represents a segment of the wave shapewhere the command signals remain invariable under different weldconditions. The system also includes a monitor circuit configured tomeasure a plurality of weld parameters generated between the advancingwire and the workpiece during the non-adaptive state at an interrogationrate over an interval of time. In addition, the system includes acircuit for calculating a plurality of quality parameters for thenon-adaptive state based on measurements of the plurality of weldparameters acquired during the interval of time within the non-adaptivestate.

In an aspect, the monitor circuit is further configured to measure theplurality of weld parameters occurring in the non-adaptive staterepeatedly at the interrogation rate for a plurality of intervals oftime, and the circuit for calculating the plurality of qualityparameters is further configured to repeatedly calculate the pluralityof quality parameters for respective measurements acquired for theplurality of intervals of time.

According to another aspect, for a quality parameter of the plurality ofquality parameters, the system further includes a circuit for comparinga value of each of the quality parameter calculated for the interval oftime to a corresponding expected quality parameter value to determine ifa difference between the value of the quality parameter calculated andthe expected quality parameter value exceeds a predetermined threshold;a circuit for weighting the value of the quality parameter calculatedwith a magnitude weight based on the difference; and a circuit forweighting the value of the quality parameter calculated with a timecontribution weight based on a time contribution of the interval of timeto the wave shape including the interval of time.

In yet another aspect, the system includes an estimation circuit formonitoring values of the command signals during the non-adaptive stateat the interrogation rate repeatedly over a plurality of intervals oftime, and for calculating noise parameters based, at least in part, ondifferences between the values of the command signals monitored and themeasurements of the plurality of weld parameters acquired during theplurality of intervals of time. Further to this aspect, the monitorcircuit is further configured to measure the plurality of weldparameters generated between the advancing wire and the workpiece duringone or more adaptive states from the series of time-segmented states andthe system can also include a filter circuit for adjusting the pluralityof weld parameters measured in the one or more adaptive states based onthe noise parameters calculated based on the non-adaptive state. Thecircuit for calculating the plurality of quality parameters is furtherconfigured to calculate the plurality of quality parameters for the oneor more adaptive states based on adjusted measurements of the pluralityof weld parameters acquired during the one or more adaptive states.

Alternatively, the system can include a circuit for calculating anadaptation model based on the noise parameters, wherein the adaptationmodel represents changes to command signals by an adaptive controller ofthe welder to account for weld conditions. Further to this aspect, themonitor circuit is further configured to measure the plurality of weldparameters generated between the advancing wire and the workpiece duringone or more adaptive states from the series of time-segmented states,and adjust the plurality of weld parameters measured in the one or moreadaptive states based on the adaptation model.

The above description of specific embodiments has been given by way ofexample. From the disclosure given, those skilled in the art will notonly understand the general inventive concepts and attendant advantages,but will also find apparent various changes and modifications to thestructures and methods disclosed. For example, the general inventiveconcepts are not typically limited to one of a manual welding process oran automated (e.g., robotic) welding process but instead are readilyadaptable to either. Furthermore, the general inventive concepts arereadily adaptable to different welding processes and techniques (e.g.,all variations of arc welding such as Stick and TIG welding). It issought, therefore, to cover all such changes and modifications as fallwithin the spirit and scope of the general inventive concepts, asdefined by the appended claims, and equivalents thereof.

What is claimed is:
 1. A method of determining a quality of a weld bymonitoring a welder as the welder performs a welding process by creatingactual welding parameters between an advancing wire and a workpiece, thewelding process being defined by a series of rapidly repeating waveshapes controlled by command signals to a power supply of the welder,said method comprising: segmenting a wave shape, having a weld cyclewith a cycle time, into a series of time-segmented states; selecting anon-adaptive state from the series of time-segmented states, wherein thenon-adaptive state represents a segment of the wave shape where thecommand signals remain invariable under different weld conditions;measuring a plurality of weld parameters generated between the advancingwire and the workpiece during the non-adaptive state at an interrogationrate over an interval of time; and calculating a plurality of qualityparameters for the non-adaptive state based on measurements of theplurality of weld parameters acquired during the interval of time withinthe non-adaptive state, the step of calculating comprising: comparing avalue of each of the quality parameter calculated for the interval oftime to a corresponding expected quality parameter value to determine ifa difference between the value of the quality parameter calculated andthe expected quality parameter value exceeds a predetermined threshold;and when the difference exceeds the predetermined threshold, weightingthe value of the quality parameter calculated with a magnitude weightbased on the difference, and weighting the value of the qualityparameter calculated with a time contribution weight based on a timecontribution of the interval of time to the wave shape including theinterval of time.
 2. The method of claim 1, further comprising:measuring the plurality of weld parameters occurring in the non-adaptivestate repeatedly at the interrogation rate for a plurality of intervalsof time; and repeatedly calculating the plurality of quality parametersfor respective measurements acquired for the plurality of intervals oftime.
 3. The method of claim 1, further comprising calculating a qualityindicator for the interval of time based on weighted values of thequality parameters.
 4. The method of claim 3, further comprising:aggregating a plurality of quality indicators respectively associatedwith a plurality of intervals of time; and calculating an overallquality indicator for the welding process based on the plurality ofquality indicators aggregated.
 5. The method of claim 1, furthercomprising utilizing the plurality of quality parameters calculated forthe non-adaptive state as training data to evaluate subsequent weldingprocesses.
 6. The method of claim 1, further comprising monitoringvalues for the command signals during the non-adaptive state at theinterrogation rate repeatedly over a plurality of intervals of time. 7.The method of claim 6, further comprising calculating noise parametersbased, at least in part, on differences between the values of thecommand signals monitored and the measurements of the plurality of weldparameters acquired during the plurality of intervals of time.
 8. Themethod of claim ,7 further comprising: measuring the plurality of weldparameters generated between the advancing wire and the workpiece duringone or more adaptive states from the series of time-segmented states;and adjusting the plurality of weld parameters measured in the one ormore adaptive states based on the noise parameters calculated based onthe non-adaptive state.
 9. The method of claim 8, further comprising:calculating the plurality of quality parameters for the one or moreadaptive states based on adjusted measurements of the plurality of weldparameters acquired during the one or more adaptive states.
 10. Themethod of claim 7, further comprising: calculating an adaptation modelbased on the noise parameters, wherein the adaptation model representschanges to command signals by an adaptive controller of the welder toaccount for weld conditions.
 11. The method of claim 10, furthercomprising: measuring the plurality of weld parameters generated betweenthe advancing wire and the workpiece during one or more adaptive statesfrom the series of time-segmented states; and adjusting the plurality ofweld parameters measured in the one or more adaptive states based on theadaptation model.
 12. A system for determining a quality of a weld bymonitoring a welder as the welder performs a welding process by creatingactual welding parameters between an advancing wire and a workpiece, thewelding process being defined by a series of rapidly repeating waveshapes controlled by command signals to a power supply of the welder,the system comprising: a logic state controller for segmenting a waveshape, having a weld cycle with a cycle time, into a series oftime-segmented states; a selection circuit for selecting a non-adaptivestate from the series of time-segmented states, wherein the non-adaptivestate represents a segment of the wave shape where the command signalsremain invariable under different weld conditions; a monitor circuitconfigured to measure a plurality of weld parameters generated betweenthe advancing wire and the workpiece during the non-adaptive state at aninterrogation rate over an interval of time; and; a circuit forcalculating a plurality of quality parameters for the non-adaptive statebased on measurements of the plurality of weld parameters acquiredduring the interval of time within the non-adaptive state; and whereinthe circuit for calculating further comprises: a circuit for comparing avalue of each of the quality parameter calculated for the interval oftime to a corresponding expected quality parameter value to determine ifa difference between the value of the quality parameter calculated andthe expected quality parameter value exceeds a predetermined threshold;a circuit for weighting the value of the quality parameter calculatedwith a magnitude weight based on the difference; and a circuit forweighting the value of the quality parameter calculated with a timecontribution weight based on a time contribution of the interval of timeto the wave shape including the interval of time.
 13. The system ofclaim 12, wherein the monitor circuit is further configured to measurethe plurality of weld parameters occurring in the non-adaptive staterepeatedly at the interrogation rate for a plurality of intervals oftime; and wherein the circuit for calculating the plurality of qualityparameters is further configured to repeatedly calculate the pluralityof quality parameters for respective measurements acquired for theplurality of intervals of time.
 14. The system of claim 12, furthercomprising an estimation circuit for monitoring values of the commandsignals during the non-adaptive state at the interrogation raterepeatedly over a plurality of intervals of time, and for calculatingnoise parameters based, at least in part, on differences between thevalues of the command signals monitored and the measurements of theplurality of weld parameters acquired during the plurality of intervalsof time.
 15. The system of claim 14, wherein the monitor circuit isfurther configured to measure the plurality of weld parameters generatedbetween the advancing wire and the workpiece during one or more adaptivestates from the series of time-segmented states, and the system furthercomprises: a filter circuit for adjusting the plurality of weldparameters measured in the one or more adaptive states based on thenoise parameters calculated based on the non-adaptive state.
 16. Thesystem of claim 15, wherein the circuit for calculating the plurality ofquality parameters is further configured to calculate the plurality ofquality parameters for the one or more adaptive states based on adjustedmeasurements of the plurality of weld parameters acquired during the oneor more adaptive states.
 17. The system of claim 14, further comprisinga circuit for calculating an adaptation model based on the noiseparameters, wherein the adaptation model represents changes to commandsignals by an adaptive controller of the welder to account for weldconditions.
 18. The system of claim 17, wherein the monitor circuit isfurther configured to measure the plurality of weld parameters generatedbetween the advancing wire and the workpiece during one or more adaptivestates from the series of time-segmented states, and adjust theplurality of weld parameters measured in the one or more adaptive statesbased on the adaptation model.